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Abstract:

In embodiments of the present invention, improved capabilities are
described for creating and using Synthetic User Identifiers within an
advertising analytic platform for the purpose of targeting the placement
of advertising within an available channel based at least in part on
Synthetic User Identifier information.

Claims:

1. A computer program product embodied in a non-transitory computer
readable medium that, when executing on one or more computers, performs
the steps of: creating, at a server facility, a plurality of synthetic
user identifiers by associating an advertisement with the advertisement's
impression data and at least two of user, device, and contextual
information as derived from a plurality of users' interactions with the
advertisement; storing the synthetic user identifiers in a database
accessible to the server facility and separate from a client system;
analyzing the plurality of synthetic user identifiers for correlations
that indicate an advertisement type may produce a predetermined
conversion rate if presented to an advertisement channel; and
recommending a targeted advertisement, which is associated with the
advertisement type, to be presented to the advertisement channel.

2. The computer program product of claim 1, wherein the step of
recommending involves recommending a bid amount for the targeted
advertisement.

3. The computer program product of claim 1, wherein the step of
recommending involves recommending a budget allocation for the targeted
advertisement.

4. The computer program product of claim 1, wherein the step of
recommending involves partitioning an advertisement inventory based on
the synthetic user identifier.

5. The computer program product of claim 1, wherein the plurality of
users' interactions with the advertisement derive from a plurality of
advertising channels.

6. The computer program product of claim 5, wherein the plurality of
advertising channels includes online and offline advertising channels.

7. The computer program product of claim 6, wherein the online
advertising channels includes a website.

8. The computer program product of claim 6, wherein the offline
advertising channels includes a print medium.

9. The computer program product of claim 1, wherein the contextual
information is a device characteristic.

10. The computer program product of claim 1, wherein the contextual
information is an operating system.

11. The computer program product of claim 1, wherein the contextual
information is an advertising medium type.

12. The computer program product of claim 1, wherein the contextual
information is a plurality of contextual information.

13. The computer program product of claim 1, wherein the contextual
information is a user demographic.

14. A computer program product embodied in a non-transitory computer
readable medium that, when executing on one or more computers, performs
the steps of: categorizing a plurality of available advertising channels,
wherein each of the available advertising channels is categorized based
at least in part on contextual information; analyzing an advertising
impression log relating to prior advertising placements within the
plurality of categorized available advertising channels, wherein the
analysis produces a quantitative association between a user and at least
one of the available advertising channels, the quantitative association
expressing at least in part a probability of the user recording an
advertising conversion within at least one of the available advertising
channels; storing the quantitative association as a synthetic user
identifier; and selecting an advertisement to present to the user within
at least one of the available advertising channels based at least in part
on the synthetic user identifier.

15. The computer program product of claim 14, wherein the selected
advertisement is presented to a second user that shares an attribute of
the user with whom the user synthetic user identifier is associated.

16. The computer program product of claim 14, wherein a failure of the
user to register a new impression following presentation of the selected
advertisement is used by a learning machine facility to update the
quantitative association.

17. The computer program product of claim 14, wherein a plurality of
synthetic user identifiers, each bearing a quantitative association with
the other, is tagged as a consumer cohort to which advertisers may bid on
the opportunity to present advertisements using a real-time bidding
machine facility.

18. The computer program product of claim 14, wherein the analysis
includes using an economic valuation model that is further based in part
on real-time bidding log data.

19. The computer program product of claim 14, wherein the analysis
includes using an economic valuation model that is further based in part
on historical bidding data.

20. A system for targeting the placement of advertising within an
available channel based at least in part on contextual parameters from an
advertising impression log, the system comprising: a computer having a
processor; software which is operable on the processor, the software
including an analytics platform facility that includes at least a
learning machine and a valuation algorithms facility, wherein the
software is adapted to: create, at a server facility, a plurality of
synthetic user identifiers by associating an advertisement with the
advertisement's impression data and at least two of user, device, and
contextual information as derived from a plurality of users' interactions
with the advertisement; store the synthetic user identifiers in a
database accessible to the server facility and separate from a client
system; use the synthetic user identifiers to target advertisements to
consumers, wherein at least one of the amount, timing or duration of
advertising presented to consumers is varied across available advertising
channels based at least in part by use of the synthetic user identifiers;
analyze the plurality of synthetic user identifiers for correlations that
indicate an advertisement type may produce a predetermined conversion
rate if advertisements are presented through an advertisement channel and
with an intensity level, wherein the intensity level is at least one of
the amount, timing or duration of the advertising presented; and
recommend, for each specific synthetic user identifier, an adjusted
intensity of advertising associated with the advertisement type, to be
presented through each advertisement channel.

Description:

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of the following United States
Provisional patent applications, each of which is hereby incorporated by
reference herein in its entirety: U.S. Provisional Patent Application
Ser. No. 61/503,682, entitled OPTIMIZED ADVERTISING YIELD MANAGEMENT AND
CONSUMER IDENTIFICATION, filed Jul. 1, 2011; and U.S. Provisional Patent
Application Ser. No. 61/649,142, entitled IMPRESSION LEVEL DATA USAGE IN
AN ONLINE ADVERTISEMENT PLACEMENT FACILITY, filed May 18, 2012.

[0002] This application is a Continuation-in-Part of the following
co-pending United States Non-Provisional patent applications, each of
which is hereby incorporated by reference herein in its entirety: United
States Non-Provisional patent application Ser. No. 12/856,547, entitled
DYNAMIC TARGETING ALGORITHMS FOR REAL-TIME VALUATION OF ADVERTISING
PLACEMENTS, filed Aug. 13, 2010; United States Non-Provisional patent
application Ser. No. 12/856,552, entitled MACHINE LEARNING FOR COMPUTING
AND TARGETING BIDS FOR THE PLACEMENT OF ADVERTISEMENTS, filed Aug. 13,
2010; United States Non-Provisional patent application Ser. No.
12/856,554, entitled USING COMPETITIVE ALGORITHMS FOR THE PREDICTION AND
PRICING OF ONLINE ADVERTISEMENT OPPORTUNITIES, filed Aug. 13, 2010;
United States Non-Provisional patent application Ser. No. 12/856,565,
entitled LEARNING SYSTEM FOR THE USE OF COMPETING VALUATION MODELS FOR
REAL-TIME ADVERTISEMENT BIDDING, filed Aug. 13, 2010; and United States
Non-Provisional patent application Ser. No. 12/856,560, entitled LEARNING
SYSTEM FOR ADVERTISING BIDDING AND VALUATION of Third Party Data, filed
Aug. 13, 2010. U.S. Non-Provisional patent application Ser. Nos.
12/856,547, 12/856,552, 12/856,554, 12/856,565, and 12/856,560 each claim
the benefit of U.S. Provisional Application Ser. No. 61/234,186, entitled
REAL-TIME BIDDING SYSTEM FOR DELIVERY OF ADVERTISING, filed Aug. 14, 2009
which is hereby incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

[0003] The invention is related to using historical and real-time data
associated with digital media and its use to adjust the pricing and
delivery of advertising media among a plurality of available advertising
channels.

BACKGROUND

[0004] The ability to measure advertising campaign results is a priority
of a majority of advertising systems. Measured advertising campaign
results, including results that are categorized by user, user groups, and
the like, may be subsequently utilized by advertisers to modify
advertising campaigns to maximize the effect of the advertisement
messages on intended user and/or user group targets. For example, an
advertiser may modify its campaigns by reallocating budgets and prices,
from lower performing ones to focus on user groups that have a history of
responsiveness to the campaign, similar campaigns, or advertisements that
share an attribute(s) with material contained within an advertising
campaign. Additionally, a plurality of media channels may be used for
communicating the advertising campaign to consumers. For online
advertising, it may be possible to measure the effect of advertisements
by using consumer identifiers stored in cookies. This enables an
advertiser to distinguish individuals, while keeping their identity
anonymous. However, there are cases where it is not possible or desirable
to distinguish individuals.

[0005] Therefore, there is a need for a method and system for providing an
advertising measurement solution for cases where it may not be possible
or desirable to identify individuals.

SUMMARY

[0006] The management of presenting advertisements to digital media users
is often characterized by a batch mode optimization scheme in which
advertising content is selected for presentation to a chosen group of
users, performance data is collected and analyzed, and optimization steps
are then carried out to better future ad performance. This process is
then iteratively run in a sequence of optimization analyses with the
intention of improving an ad performance criterion, such as a completed
transaction, through more informed ad-user pairings and other techniques.
However, this optimization framework is limited in several important
respects. For example, given the growth of digital media users brought
about by popular innovations such as social networking, there is an
over-abundance of data relating to digital media usage that cannot be
accommodated and analyzed by the pre-planned, batch mode analytics of
much of the current advertising performance modeling conducted in the
industry. Furthermore, the batch mode of advertising analytics may force
content groupings that do not correspond to the actual, and
ever-changing, ad impression sequences that are occurring within a user's
behavior, or across a pool of users. As a result, publishers of
advertising content may be forced to unnecessarily utilize a number of ad
networks to distribute their advertisements based at least in part on the
plurality of optimization techniques and criteria used by the different
ad networks. This may create redundancies and limit the ability to value
the worth of an advertisement's impression and its performance over time
within the totality of digital media users.

[0007] In embodiments, the present invention may provide methods and
systems for creating, at a server facility, a plurality of Synthetic User
Identifiers by associating an advertisement with the advertisement's
impression data and at least two of user, device, and contextual
information as derived from a plurality of users' interactions with the
advertisement. The Synthetic User Identifiers may be stored in a database
that is accessible to the server facility and separate from a client
system. The plurality of Synthetic User Identifiers may be analyzed for
correlations that indicate an advertisement type may produce a
predetermined conversion rate if presented to an advertisement channel,
and a targeted advertisement may be recommended, which is associated with
the advertisement type, to be presented to the advertisement channel.

[0008] In embodiments, the step of recommending may involve recommending a
bid amount for the targeted advertisement, recommending a budget
allocation for the targeted advertisement, or some other type of
recommendation. Recommending may involve partitioning an advertisement
inventory based on the Synthetic User Identifier.

[0009] In embodiments, the plurality of users' interactions with the
advertisement may derive from a plurality of advertising channels. The
plurality of advertising channels may include online and offline
advertising channels. Online advertising channels may include a website.
Offline advertising channels may include a print medium.

[0010] In embodiments, contextual information may be a device
characteristic, an operating system, an advertising medium type, a
plurality of contextual information, a user demographic, or some other
type of contextual information.

[0011] In embodiments, the present invention may provide methods and
systems for categorizing a plurality of available advertising channels,
wherein each of the available advertising channels is categorized based
at least in part on contextual information. An advertising impression log
relating to prior advertising placements within the plurality of
categorized available advertising channels may be analyzed, wherein the
analysis produces a quantitative association between a user and at least
one of the available advertising channels, the quantitative association
expressing at least in part a probability of the user recording an
advertising conversion within at least one of the available advertising
channels. The quantitative association may be stored as a Synthetic User
Identifier, and an advertisement may be selected to present to the user
within at least one of the available advertising channels based at least
in part on the Synthetic User Identifier.

[0012] In embodiments, the selected advertisement may be presented to a
second user that shares an attribute of the user with whom the user
Synthetic User Identifier is associated.

[0013] In embodiments, a failure of the user to register a new impression
following presentation of the selected advertisement is used by a
learning machine facility to update the quantitative association.

[0014] In embodiments, a plurality of Synthetic User Identifiers, each
bearing a quantitative association with the other, may be tagged as a
consumer cohort to which advertisers may bid on the opportunity to
present advertisements using a real-time bidding machine facility. The
analysis may include using an economic valuation model that is further
based in part on real-time bidding log data. The analysis may include
using an economic valuation model that is further based in part on
historical bidding data.

[0015] In embodiments, the present invention may provide methods and
systems for targeting the placement of advertising within an available
channel based at least in part on contextual information, the system
comprising: a computer having a processor and software which is operable
on the processor. The software may include an analytics platform facility
that includes at least a learning machine and a valuation algorithms
facility. The software may be adapted to: (i) create, at a server
facility, a plurality of Synthetic User Identifiers by associating an
advertisement with the advertisement's impression data and at least two
of user, device, and contextual information as derived from a plurality
of users' interactions with the advertisement; (ii) store the Synthetic
User Identifiers in a database accessible to the server facility and
separate from a client system; (iii) use the Synthetic User Identifiers
to target advertisements to consumers, wherein at least one of the
amount, timing or duration of advertising presented to consumers is
varied across available advertising channels based at least in part by
use of the Synthetic User Identifiers; (iv) analyze the plurality of
Synthetic User Identifiers for correlations that indicate an
advertisement type may produce a predetermined conversion rate if
advertisements are presented through an advertisement channel and with an
intensity level, wherein the intensity level is at least one of the
amount, timing or duration of the advertising presented; and (v)
recommend, for each specific Synthetic User Identifiers, an adjusted
intensity of advertising associated with the advertisement type, to be
presented through each advertisement channel.

[0016] While the invention has been described in connection with certain
preferred embodiments, other embodiments would be understood by one of
ordinary skill in the art and are encompassed herein.

BRIEF DESCRIPTION OF THE FIGURES

[0017] The invention and the following detailed description of certain
embodiments thereof may be understood by reference to the following
figures:

[0018] FIG. 1A depicts a real-time bidding method and system for the
delivery of advertising.

[0019]FIG. 1B depicts the execution of the real-time bidding system
across multiple exchanges.

[0020] FIG. 2 depicts a learning method and system for optimizing bid
management.

[0021] FIG. 3 depicts sample data domains that may be used to predict
media success associated with key performance indicators.

[0022] FIG. 4 depicts training multiple algorithms relating to an
advertising campaign, in which better performing algorithms may be
detected.

[0023]FIG. 5A depicts the use of micro-segmentation for bid valuation.

[0043] FIG. 21 illustrates a method for predicting performance of
advertising placements based on current market conditions

[0044]FIG. 22 illustrates a method for determining a preference between a
primary model and a second model for predicting economic valuation.

[0045]FIG. 23 illustrates a method for determining a preference between a
primary model and a second model for predicting economic valuation.

[0046]FIG. 24 illustrates a method for selecting one among multiple
competing valuation models in real-time bidding for advertising
placements.

[0047]FIG. 25 illustrates a method for replacing a first economic
valuation model by a second economic valuation model for deriving a
recommended bid amount for an advertising placement.

[0048]FIG. 26 illustrates a method for evaluating multiple economic
valuation models and selecting one valuation as a future valuation of an
advertising placement.

[0049] FIG. 27 illustrates a method for evaluating in real time multiple
economic valuation models and selecting one valuation as a future
valuation of an advertising placement.

[0050]FIG. 28 illustrates a method for evaluating multiple bidding
algorithms to select a preferred algorithm for placing an advertisement.

[0051]FIG. 29 illustrates a method for replacing a bid recommendation
with a revised bid recommendation for an advertising placement.

[0052]FIG. 30 illustrates a real-time facility for measuring the value of
additional third party data.

[0053]FIG. 31 illustrates a method for advertising valuation that has the
ability to measure the value of additional third party data.

[0054] FIG. 32 illustrates a method for computing a valuation of a third
party dataset and billing an advertiser a portion of the valuation.

[0055]FIG. 33 illustrates a method for computing a valuation of a third
party dataset and calibrating a bid amount recommendation for a publisher
to pay for a placement of an ad content based at least in part on the
valuation.

[0056] FIG. 34 depicts a data visualization embodiment presenting a
summary of advertising performance by time of day versus day of the week.

[0076] FIG. 54 shows an embodiment of a process flow from an RTB branding
bidding function, to a campaign, survey, responses, and valuation
algorithms leading to an optimization engine.

[0077] FIGS. 55-56 illustrate embodiments of how exposed market increments
may be adjusted as survey results tally from a campaign.

[0078]FIG. 57 illustrates a method of creating a plurality of Synthetic
User Identifiers that may be used to select a targeted advertisement.

[0079]FIG. 58 illustrates a method of creating and using a Synthetic User
Identifier to present an advertisement to a user.

[0080]FIG. 59 illustrates a system for varying the intensity level of
advertising based on a plurality of Synthetic User Identifiers.

DETAILED DESCRIPTION

[0081] Referring to FIG. 1A, a real-time bidding system 100A that may be
used according to the methods and systems as described herein for
selecting and valuing sponsored content buying opportunities, real-time
bidding, and placing sponsored content, such as advertisements, across a
plurality of content delivery channels. The real-time bidding facility
may inform buying opportunities to place sponsored content across
multiple advertisement ("ad") delivery channels. The real-time bidding
facility may further enable the collection of data regarding ad
performance and use this data to provide ongoing feedback to parties
wanting to place ads, and automatically adjust and target the ad delivery
channels used to present sponsored content. The real-time bidding system
100A may facilitate the selection of a particular ad type to show in each
placement opportunity, and the associated costs of the ad placements over
time (and, for example, adjusted by time of placement). The real-time
facility may facilitate valuation of ads, using valuation algorithms, and
may further optimize return on investment for an advertiser 104.

[0082] The real-time bidding system 100A may include, and/or be further
associated with, one or more distribution service consumers, such as an
advertising agency 102 or advertiser 104, an ad network 108, an ad
exchange 110, or a publisher 112, an analytics facility 114, an ad
tagging facility 118, an advertising order sending and receiving facility
120, and advertising distribution service facility 122, an advertising
data distribution service facility 124, an ad display client facility
128, an advertising performance data facility 130, a contextualizer
service facility 132, a data integration facility 134, and one or more
databases providing different types of data relating to ads and/or ad
performance. In an embodiment of the invention, the real-time bidding
system 100A may include an analytic facility that may, at least in part,
include a learning machine facility 138, a valuation algorithms facility
140, a real-time bidding machine facility 142, a tracking machine
facility 144, an impression/click/action logs facility 148, and a
real-time bidding logs facility 150.

[0083] In embodiments, the one or more databases providing data to the
real-time bidding system 100A and to the learning machine facility 138
relating to ads, ad performance, or ad placement context, may include an
agency database and/or an advertiser database 152. The agency database
may include campaign descriptors, and may describe the channels,
timelines, budgets, and other information, including historical
information, relating to the use and distribution of advertisements. The
agency data 152 may also include campaign and historic logs that may
include the placement for each advertisement shown to users. The agency
data 152 may also include one or more of the following: an identifier for
the user, the web page context, time, price paid, ad message shown, and
resulting user actions, or some other type of campaign or historic log
data. The advertiser database may include business intelligence data, or
some other type of data, which may describe dynamic and/or static
marketing objectives, or may describe the operation of the advertiser
104. In an example, the amount of overstock of a given product (that the
advertiser 104 has in its warehouses) may be described by the advertiser
data 152. In another example, the data may describe purchases executed by
costumers when interacting with the advertiser 104,

[0084] In embodiments, the one or more databases may include an historic
event database. The historic event data 154, may be used to correlate the
time of user events with other events happening in, for example, a region
in which the user is located. In an example, response rates to certain
types of advertisements may be correlated to stock market movements. The
historic event data 154 may include, but is not limited to, weather data,
events data, local news data, or some other type of data.

[0085] In embodiments, the one or more databases may include a user data
158, database. The user data 158, may include data may be internally
sourced and/or provided by third parties that may contain personally
linked information about advertising recipients. This information may
associate users with preferences, or other indicators, which may be used
to label, describe, or categorize the users.

[0086] In embodiments, the one or more databases may include a real-time
event database. The real-time event data 160 may include data similar to
historic data, but more current. The real-time event data 160 may
include, but is not limited to, data that is current to the second,
minute, hour, day, or some other measure of time. In an example, if the
learning machine facility 138 finds a correlation between ad performance
and historic stock market index values, the real-time stock market index
value may be used to valuate advertisements by the real-time bidding
machine facility 142.

[0087] In embodiments, the one or more databases may include a contextual
database that may provide contextual data 162, associated with
publisher's, publisher's content (e.g., a publisher's website), and the
like. Contextual data 162, may include, but is not limited to, keywords
found within the ad; an URL associated with prior placements of the ad,
or some other type of contextual data 162, and may be stored as a
categorization metadata relating to publisher's content. In an example,
such categorization metadata may record that a first publisher's website
is related to financial content, and a second publisher's content is
predominantly sports-related.

[0088] In embodiments, the one or more databases may further include a
third party/commercial database. A third party/commercial database may
include data 164, relating to consumer transactions, such as
point-of-sale scanner data obtained from retail transactions, or some
other type of third party or commercial data.

[0089] In embodiments of the present invention, data from the one or more
databases may be shared with the analytic facilities 114, of the
real-time bidding system 100A through a data integration facility 134. In
an example, the data integration facility 134 may provide data from the
one or more databases to the analytics facilities of the real-time
bidding system 100A for the purposes of evaluating a potential ad and/or
ad placement. For example, the data integration facility 134, may
combine, merge, analyze or integrate a plurality of data types received
from the available databases (e.g., user data 158 and real-time event
data 160). In an embodiment, a contextualizer may analyze web content to
determine whether a web page contains content about sports, finance, or
some other topic. This information may be used as an input to the
analytics platform facility 114 in order to identify the relevant
publishers and/or web pages where ads will appear.

[0090] In embodiments, the analytics facilities of the real-time bidding
system 100A may receive an ad request via the advertising order sending
and receiving facility 120. The ad request may come from an advertising
agency 102, advertiser 104, ad network 108, ad exchange 110, and
publisher 112 or some other party requesting advertising content. For
example, the tracking machine facility 144 may receive the ad request via
the advertising order sending and receiving facility 120, and provide a
service that may include attaching an identifier, such as an ad tag using
an ad tagging facility 118, to each ad order, and resulting ad placement.
This ad tracking functionality may enable the real-time bidding system
100A to track, collect and analyze advertising performance data 130. For
example an online display ad may be tagged using a tracking pixel. Once a
pixel is served from the tracking machine facility 144, it may record the
placement opportunity as well as the time and date of the opportunity. In
another embodiment of the invention, the tracking machine facility 144
may record the ID of the ad requestor, the user, and other information
that labels the user including, but not limited to, Internet Protocol
(IP) address, context of an ad and/or ad placement, a user's history,
geo-location information of the user, social behavior, inferred
demographics or some other type of data Ad impressions, user
clickthroughs, action logs, or some other type of data, may be produced
by the tracking machine facility 144.

[0091] In embodiments, the recorded logs, and other data types, may be
used by the learning machine facility 138 to improve and customize the
targeting and valuation algorithms 140, as described herein. The learning
machine facility 138 may create rules regarding advertisements that are
performing well for a given client and may optimize the content of an
advertising campaign based on the created rules. Further, in embodiments
of the invention, the learning machine facility 138 may be used to
develop targeting algorithms for the real-time bidding machine facility
142. The learning machine facility 138 may learn patterns, including
Internet Protocol (IP) address, context of an ad and/or ad placement, URL
of the ad placement website, a user's history, geo-location information
of the user, social behavior, inferred demographics, or any other
characteristic of the user or that can be linked to the user, ad concept,
ad size, ad format, ad color, or any other characteristic of an ad or
some other type of data, among others, that may be used to target and
value ads and ad placement opportunities. In an embodiment of the
invention, the learning patterns may be used to target ads. Further, the
learning machine facility 138 may be coupled to one or more databases, as
depicted in FIG. 1, from which it may obtain additional data needed to
further optimize targeting and/or valuation algorithms 140.

[0092] In an embodiment of the invention, an advertiser 104 may place an
"order" with instructions limiting where and when an ad may be placed.
The order from the advertiser 104 may be received by the learning machine
facilities or another element of the platform. The advertiser 104 may
specify the criteria of `goodness` for the ad campaign to be successful.
Further, the tracking machine facility 144 may be used to measure the
`goodness` criteria. The advertiser 104 may also provide historic data
associated with the `order` in order to bootstrap the outcome of the
analysis. Thus, based on data available from the one or more databases
and the data provided by the advertiser 104, the learning machine
facility 138 may develop customized targeting algorithms for the
advertisement. The targeting algorithms may calculate an expected value
of the advertisement under certain conditions (using, for example,
real-time event data 160 as part of the modeling). The targeting
algorithms may also seek to maximize the specified `goodness` criteria.
The targeting algorithms developed by the learning machine facility 138
may be received by the real-time bidding machine 142, which may wait for
opportunities to place the advertisement. In an embodiment of the
invention, the real-time bidding machine facility 142 may also receive an
ad and/or bid request via the advertising order sending and receiving
facility 120. The real-time bidding machine facility 142 may be
considered a "real-time" facility since it may reply to an ad or bid
request that is associated with a time constraint. The real-time bidding
machine facility 142 may use a non-stateless method to calculate which
advertising message to show, while the user waits for the system to
decide. The real-time bidding machine facility 142 may perform the
real-time calculation using algorithms provided by the learning machine
facility 138, dynamically estimating an optimal bid value. In
embodiments, an alternative real-time bidding machine facility 142 may
have a stateless configuration to determine an advertisement to present.

[0093] The real-time bidding machine facility 142 may blend historical and
real-time data to produce a valuation algorithm for calculating a
real-time bid value to associate with an ad and/or ad placement
opportunity. The real-time bidding machine facility 142 may calculate an
expected value that combines information about the Internet Protocol (IP)
address, context of an ad and/or ad placement, a user's history,
geo-location information of the user, social behavior, inferred
demographics or some other type of data. In embodiments, the real-time
bidding machine facility 142 may use an opportunistic algorithm update by
using tracking machine 144 or ad performance data to order and prioritize
the algorithms based at least in part on the performance of each
algorithm. The learning machine facility 138 may use and select from an
open list of multiple, competing algorithms in the machine learning
facility and real-time bidding facility. The real-time bidding machine
142 may use control systems theory to control the pricing and speed of
delivery of a set of advertisements. Further, the real-time bidding
machine facility 142 may use won and lost bid data to build user
profiles. Also, the real-time bidding machine 142 may correlate expected
values with current events in the ad recipient's geography. The real-time
bidding machine facility 142 may trade ad buys across multiple exchanges
and thus, treat multiple exchanges as a single source of inventory,
selecting and buying ads based at least in part on the valuation that is
modeled by the real-time bidding system 100A.

[0094] In embodiments, the real-time bidding system 100A may further
include a real-time bidding log facility that may record a bid request
received and a bid response sent by the real-time bidding machine
facility 142. In an embodiment of the invention, the real-time bidding
log may log additional data related to a user. In an example, the
additional data may include the details of the websites the user may
visit. These details may be used to derive user interests or browsing
habits. Additionally, the real-time bidding log facility may record the
rate of arrival of advertising placement opportunities from different ad
channels. In an embodiment of the invention, the real-time bidding log
facility may also be coupled to the learning machine facility 138.

[0095] In embodiments, the real-time bidding machine 142 may dynamically
determine an anticipated economic valuation for each of the plurality of
potential placements for an advertisement based at least in part on
valuation algorithms 140 associated with the learning machine facility
138. In response to receiving a request to place an advertisement, the
real-time bidding machine facility 142 may dynamically determine an
anticipated economic valuation for each of the plurality of potential
placements for the advertisement, and may select and decide whether to
present the available placements based on the economic valuation to the
one or more distribution service consumers.

[0096] In embodiments, the real-time bidding machine 142 may include
altering a model for dynamically determining the economic valuation prior
to processing a second request for a placement. The alteration of the
model may be based at least in part on a valuation algorithm associated
with the learning facility. In an embodiment of the invention, prior to
selecting and presenting the one or more of the available placements, the
behavior of an economic valuation model may be altered to produce a
second set of valuations for each of the plurality of placements.

[0097] In embodiments, the valuation algorithms 140 may evaluate
performance information relating to each of the plurality of ad
placements. A dynamically variable economic valuation model may be used
to determine the anticipated valuation. The valuation model may evaluate
bid values in relation to the economic valuations for a plurality of
placements. A step in bidding for the plurality of available placements
and/or plurality of advertisements may be based on the economic
valuation. In an exemplary case, the real-time bidding machine facility
142 may adopt the following sequence: At Step 1, the real-time bidding
machine 142 may filter possible ads that are to be shown using the
valuation algorithms 140. At Step 2, the real-time bidding machine
facility 142 may check if the filtered ads have remaining budget funds,
and may remove any ads from the list that do not have available budget
funds from the list. At Step 3, the real-time bidding machine facility
142 may run an economic valuation algorithm for the ads in order to
determine the economic value for each ad. At Step 4, the real-time
bidding machine 142 may adjust the economic values by the opportunity
cost of placing an ad. At Step 5, the real-time bidding machine facility
142 may select the ad with the highest economic value, after adjusting by
the opportunity cost. At Step 6, the information about the first request,
which may include information about the publisher 112 content of a
request, may be used to update the dynamic algorithm before the second
request is received and processed. Finally, at Step 7, the second ad may
be processed in the same sequence as the first, with updates to the
dynamic algorithm before the third ad is placed. In embodiments, a
plurality of competing valuation algorithms 140 may be used at each step
in selecting an ad to present. By tracking the advertising performance of
the ad that eventually is placed, the competing algorithms may be
evaluated in order to determine their relative performance and utility.

[0098] In an embodiment of the present invention, competing algorithms may
be tested by dividing portions of data into separate training and
validation sets. Each of the algorithms may be trained on a training set
of data, and then validated (measured) for predictiveness against the
validation set of data. Each bidding algorithm may be evaluated for its
predictiveness against the validation set using metrics such as receiver
operating characteristic (ROC) area, Lift, Precision/Recall, Return on
Advertising Spend, other signal processing metrics, other machine
learning metrics, other advertising metrics, or some other analytic
method, statistical technique or tool. It will be understood that general
analytic methods, statistical techniques, and tools for evaluating
competing algorithms and models, such as valuation models, as well as
analytic methods, statistical techniques, and tools known to a person of
ordinary skill in the art are intended to be encompassed by the present
invention and may be used to evaluate competing algorithms and valuation
models in accordance with the methods and systems of the present
invention. Predictiveness of an algorithm may be measured by how well it
predicts the likelihood that showing a particular advertisement to a
particular consumer in a particular context is likely to influence a
consumer to engage in a desirable action, such as purchasing one of the
advertiser's products, engaging with the advertiser product, affecting
the consumer perception about the advertiser's product, visiting a web
page, or taking some other kind of action which is valued by the
advertiser.

[0099] In an embodiment of the present invention, cross-validation may be
used to improve the algorithm evaluation metrics. Cross-validation
describes a methodology where a training set-validation set procedure for
evaluating competing algorithms and/or models is repeated multiple times
by changing the training and validation sets of data. Cross-validation
techniques that may be used as part of the methods and systems described
herein include, but are not limited to, repeated random sub-sampling
validation, k-fold cross-validation, k×2 cross-validation,
leave-one-out cross-validation, or some other type of cross-validation
technique.

[0100] In embodiments, competing algorithms may be evaluated using the
methods and systems as described herein, in real-time, in batch mode
processing, or using some other periodic processing framework. In
embodiments, competing algorithms may be evaluated online, such as using
the Internet or some other networked platform, or the competing
algorithms may be evaluated offline and made available to an online
facility following evaluation. In a sample embodiment, one algorithm may
be strictly better than all other algorithms, in terms of its
predictiveness, and it may be chosen offline in the learning facility
138. In another sample embodiment, one algorithm from a set may be more
predictive given a particular combination of variables, and more than one
algorithm may be made available to the real-time bidding facility 142 and
the selection of the best performing algorithm may take place in
real-time, for example, by examining the attributes of a particular
placement request, then determining which algorithm from the set of
trained algorithms is most predictive for that particular set of
attributes.

[0101] In embodiments, data corresponding to the valuation of an ad from
the real-time bidding system 100A may be received by the advertising
distribution service facility 122 and delivered to a consumer of the
valuation data, such as an advertising agency 102, advertiser 104, ad
network 108, ad exchange 110, publisher 112, or some other type of
consumer. In another embodiment of the invention, the advertising
distribution service facility 122 may be an ad server. The advertising
distribution service facility 122 may distribute an output of the
real-time bidding system 100A, such as a selected ad, to the one or more
ad servers. In embodiments, the advertising distribution service
facilities 122 may be coupled to the tracking machine facility 144. In
another embodiment of the invention, the advertising distribution service
facility 122 may be coupled to an ad display client 128. In embodiments,
an ad display client 128 may be a mobile device, a PDA, cell phone, a
computer, a communicator, a digital device, a digital display panel or
some other type of device able to present advertisements.

[0102] In embodiments, an ad received at the ad display client 128 may
include interactive data; for example, popping up of an offer on movie
tickets. A user of the ad display client 128 may interact with the ad and
may perform actions such as making a purchase, clicking an ad, filling
out a form, or performing some other type of user action. The user
actions may be recorded by the advertising performance data facility 130.
In an embodiment, the advertising performance data facility 130 may be
coupled to the one or more databases. In an example, the performance data
facility may be coupled to the contextual database for updating the
contextual database in real-time. In an embodiment, the updated
information may be accessed by the real-time bidding system 100A for
updating the valuation algorithms 140. In embodiments, the advertising
performance data facility 130 may be coupled to the one or more
distribution service consumers.

[0103] Data corresponding to the valuation of an ad from the analytics
platform facility 114 may also be received by the advertising
distribution service facility 122. In an embodiment of the invention, the
advertising distribution service facility 122 may utilize the valuation
data for reordering/rearranging/reorganizing the one or more ads. In
another embodiment, the advertising distribution service facility 122 may
utilize the valuation data for ranking ads based on predefined criteria.
The predefined criteria may include, time of the day, location, and the
like.

[0104] The advertising data distribution service facility 124 may also
provide valuation data to the one or more consumers of ad valuation data.
In embodiments, an advertising data distribution service facility 124 may
sell the valuation data or may provide subscription of the valuation data
to the one or more consumers of ad valuation data. In embodiments, the
advertising distribution service facility 122 may provide the output from
the real-time bidding system 100A or from the learning machine facility
138 to the one or more consumers of ad valuation data. The consumers of
ad valuation data may include, without any limitation, advertising
agencies 102/advertisers 104, an ad network 108, an ad exchange 110, a
publisher 112, or some other type of ad valuation data customer. In an
example, an advertising agency 102 may be a service business dedicated to
creating, planning, and handling of advertisements for its clients. The
ad agency 102 may be independent from the client and may provide an
outside point of view to the effort of selling the client's products or
services. Further, the ad agencies 102 may be of different types,
including without any limitation, limited-service advertising agencies,
specialist advertising agencies, in-house advertising agencies,
interactive agencies, search engine agencies, social media agencies,
healthcare communications agencies, medical education agencies, or some
other type of agency. Further, in examples, an ad network 108 may be an
entity that may connect advertisers 104 to websites that may want to host
their advertisements. Ad networks 108 may include, without any
limitation, vertical networks, blind networks, and targeted networks. The
Ad networks 108 may also be classified as first-tier and second-tier
networks. The first-tier advertising networks may have a large number of
their own advertisers 104 and publishers, they may have high quality
traffic, and they may serve ads and traffic to second-tier networks. The
second-tier advertising networks may have some of their own advertisers
104 and publishers, but their main source of revenue may come from
syndicating ads from other advertising networks. An ad exchange 110
network may include information related to attributes of ad inventory
such as price of ad impression, number of advertisers 104 in a specific
product or services category, legacy data about the highest and the
lowest bid for a specific period, ad success (user click the ad
impression), and the like. The advertisers 104 may be able to use this
data as part of their decision-making. For example, the stored
information may depict the success rate for a particular publisher 112.
In addition, advertisers 104 may have an option of choosing one or more
models for making financial transactions. For example, a
cost-per-transaction pricing structure may be adopted by the advertiser
104. Likewise, in another example, advertisers 104 may have an option to
pay cost-per-click. The ad exchange 110 may implement algorithms, which
may allow the publisher 112 to price ad impressions during bidding in
real-time.

[0105] In embodiments, a real-time bidding system 100A for advertising
messages delivery may be a composition of machines intended for buying
opportunities to place advertising messages across multiple delivery
channels. The system may provide active feedback in order to
automatically fine-tune and target the channels used to present the
advertising messages, as well as to select what advertising messages to
show in each placement opportunity, and the associated costs over time.
In embodiments, the system may be composed of interconnected machines,
including but not limited to: (1) a learning machine facility 138, (2) a
real-time bidding machine 142, and (3) a tracking machine 144. Two of the
machines may produce logs, which may be internally used by the learning
machine facility 138. In embodiments, the inputs to the system may be
from both real-time and non-real time sources. Historical data may be
combined with real-time data to fine-tune pricing and delivery
instructions for advertising campaigns.

[0107] In embodiments, agencies and/or advertisers 104 may provide
historical ad data, and may be beneficiaries of the real-time bidding
system 100A.

[0108] In embodiments, agency data 152, such as campaign descriptors, may
describe the channels, times, budgets, and other information that may be
allowed for diffusion of advertising messages.

[0109] In embodiments, agency data 152, such as campaign and historic logs
may describe the placement for each advertising message show to a user,
including one or more of the following: an identifier for the user, the
channel, time, price paid, ad message shown, and user resulting user
actions, or some other type of campaign or historic log data. Additional
logs may also record spontaneous user actions, for example a user action
that is not directly traceable to an advertising impression, or some
other type of spontaneous user action.

[0110] In embodiments, advertiser data 152 may consist of business
intelligence data, or some other type of data, that describes dynamic
and/or static marketing objectives. For example, the amount of overstock
of a given product that the advertiser 104 has in its warehouses may be
described by the data.

[0111] In embodiments, key performance indicators may include a set of
parameters that expresses the `goodness` for each given user action. For
example, a product activation may be valued at $X, and a product
configuration may be valued at $Y.

[0112] In embodiments, historic event data 154 may be used by the
real-time bidding system 100A to correlate the time of user events with
other events happening in their region. For example, response rates to
certain types of advertisements may be correlated to stock market
movements. Historic event data 154 may include, but is not limited to
weather data, events data, local news data, or some other type of data.

[0113] In embodiments, user data 158 may include data provided by third
parties that contains personally linked information about advertising
recipients. This information may show users preferences, or other
indicators, that label or describe the users.

[0114] In embodiments, a contextualizer service 132 may identify the
contextual category of a medium for advertising. For example, a
contextualizer may analyze web content to determine whether a web page
contains content about sports, finance, or some other topic. This
information may be used as an input to the learning system 138, to refine
which types of pages on which ads will appear.

[0115] In embodiments, real-time event data 160 may include data similar
to historic data, but that is more current. Real-time event data 160 may
include, but is not limited to data that is current to the second,
minute, hour, day, or some other measure of time. For example, if the
learning machine facility 138 finds a correlation between ad performance
and historic stock market index values, the real-time stock market index
value may be used to value advertisements by the real-time bidding
machine 142.

[0116] In embodiments, an advertising distribution service 122 may
include, but is not limited to ad networks 108, ad exchanges 110,
sell-side optimizers, or some other type of advertising distribution
service 122.

[0117] In embodiments, an advertising recipient may include a person who
receives an advertising message. Advertising content may be specifically
requested ("pulled") as part of or attached to content requested by an
advertising recipient, or "pushed" over the network by, for example, an
advertising distribution service 122. Some non-limiting examples of modes
of receiving advertising include the Internet, mobile phone display
screens, radio transmissions, television transmissions, electronic
bulletin boards, printed media, and cinematographic projections.

[0118] In embodiments, a real-time bidding system 100A for advertising
messages delivery may include internal machines and services. Internal
machines and services may include, but are not limited to, a real-time
bidding machine 142, a tracking machine 144, a real-time bidding log,
impression, click and action logs, a learning machine facility 138, or
some other type of internal machine and/or service.

[0119] In embodiments, a real-time bidding machine 142 may receive a bid
request message from an advertising distribution service 122. A real-time
bidding machine 142 may be considered a "real-time" system, since it may
reply to a bid request that is associated with a time constraint. The
real-time bidding machine 142 may use a non-stateless method to calculate
which advertising message to show, while the user is waiting for the
system to decide. The system may perform the real-time calculation using
algorithms provided by the learning machine facility 138, dynamically
estimating an optimal bid value. In embodiments, an alternative system
may have a stateless configuration to determine an advertisement to
present.

[0120] In embodiments, a tracking machine 144 may provide a service that
will attach tracking IDs to each advertisement. For example, an online
display ad may be followed by a pixel. Once a pixel is served from the
tracking machine 144, it may record the placement opportunity as well as
the time and date; additionally, the machine may record the ID of the
user, and other information that labels the user, including but not
limited to IP address, geographic location, or some other type of data.

[0121] In embodiments, a real-time bidding log may record a bid request
received and a bid response sent by the real-time bidding machine 142.
This log may contain additional data about which sites a user has visited
that could be used to derive user interests or browsing habits.
Additionally, this log may record the rate of arrival of advertising
placement opportunities from different channels.

[0122] In embodiments, impression, click and action logs may be records
that are produced by the tracking system, which can be used by the
learning machine facility 138.

[0123] In embodiments, a learning machine facility 138 may be used to
develop targeting algorithms for the real-time bidding machine 142. The
learning machine facility 138 may learn patterns, including social
behavior, inferred demographics, among others, that may be used to target
online ads.

[0124] In an example, an advertiser 104 may place an "order" with
instructions limiting where and when an ad may be placed. The order may
be received by the learning machine facility 138. The advertiser 104 may
specify the criteria of `goodness` for the campaign to be successful.
Such `goodness` criteria may be measurable using the tracking machine
144. The advertiser 104 may provide historic data to bootstrap the
system. Based on available data, the learning system 138 may develop
customized targeting algorithms for the advertisement. The algorithms may
calculate an expected value of the advertisement given certain
conditions, and seek to maximize the specified `goodness` criteria.
Algorithms may be received by the real-time bidding machine 142, which
may wait for opportunities to place the advertisement. Bid requests may
be received by the real-time bidding machine 142. Each one may be
evaluated for its value for each advertiser 104, using the received
algorithms. Bid responses may be sent for ads that have an attractive
value. Lower values may be bid if estimated appropriate. The bid response
may request that an ad be placed at a particular price. Ads may be tagged
with a tracking system, such as a pixel displayed in a browser. The
tracking machine 144 may log ad impressions, user clicks, and user
actions. And/or other data. The tracking machine logs may be sent to the
learning system 138, which may use the `goodness criteria,` and decide
which algorithms to improve, and further customize them. This process may
be iterative. The system may also correlate expected values with current
events in the ad recipient's geo-region.

[0126] In embodiments, a real-time bidding machine 142 may blend
historical and real-time data to produce an algorithm for calculating a
real-time bid value.

[0127] In embodiments, a real-time bidding machine 142 may calculate an
expected value that combines information about the context of an ad
placement, a user's history and geo-location information, and the ad
itself, or some other type of data, to calculate an expected value of
showing a particular advertisement at a given time.

[0128] In embodiments, a real-time bidding machine 142 may use algorithms
rather than targeting "buckets."

[0129] In embodiments, a real-time bidding machine 142 may use an
opportunistic algorithm update, by using tracking machine facility 144
feedback to prioritize the worst performing algorithms.

[0130] In embodiments, a real-time bidding machine 142 may use an open
list of multiple, competing algorithms in the learning system 138 and
real-time bidding system 100A.

[0131] In embodiments, a real-time bidding machine 142 may use control
systems theory to control the pricing and speed of delivery of a set of
advertisements.

[0132] In embodiments, a real-time bidding machine 142 may use won and
lost bid data to build user profiles.

[0133] As shown in FIG. 1B, in embodiments, a real-time bidding machine
may trade ad buys across multiple exchanges 100B. Treating multiple
exchanges as a single source of inventory.

[0134] Referring to FIG. 2, the analytic algorithms of the real-time
bidding system may be used to optimize the management of bids associated
with advertisements and advertisement impressions, conversions, or some
other type of ad-user interaction 200. In embodiments, the learning
system embodied, for example, by the learning machine 138 may create
rules regarding which advertisements are performing well for a given
client and optimize the content mix of an advertising campaign based at
least in part on the rules. In an example, a digital media user's
behavior, such as an advertisement clickthrough, impression, webpage
visit, transaction or purchase, or third party data associated with the
user may be associated with, and used by the learning system of the
real-time bidding system. The real-time bidding system may use the output
of the learning system (e.g., rules and algorithms) to pair a request for
an advertisement with an advertisement selection that conforms to the
rules and/or algorithms created by the learning machine. A selected
advertisement may come from an ad exchange, inventory partner, or some
other source of advertising content. The selected advertisement may then
be associated with an ad tag, as described herein, and sent to the
digital media user for presentation, such as on a webpage. The ad tag may
then be tracked and future impressions, clickthroughs, and the like
recorded in databases associated with the real-time bidding system. The
rules and algorithms may then be further optimized by the learning
machine based at least in part on new interactions (or lack thereof)
between the selected advertisement and the digital media user.

[0135] In embodiments, a computer program product embodied in a computer
readable medium that, when executing on one or more computers, may
dynamically determine an anticipated economic valuation for each of a
plurality of potential placements for an advertisement based at least in
part on receiving a request to place an advertisement for a publisher. In
response to receiving a request to place an advertisement for a
publisher, the method and system of the present invention may dynamically
determine an anticipated economic valuation for each of a plurality of
potential placements for the advertisement, and/or plurality of
advertisements, and select and decide whether to present to the publisher
at least one of the plurality of available placements and/or plurality of
advertisements based on the economic valuation.

[0136] In embodiments, the method and system enabled by the computer
program may comprise altering a model for dynamically determining the
economic valuation prior to processing a second request for a placement.
Alteration of the model may be based at least in part on machine
learning.

[0137] In embodiments, prior to selecting and presenting at least one of
the plurality of available placements, and/or plurality of
advertisements, the behavior of an economic valuation model may be
altered to produce a second set of valuations for each of the plurality
of placements, wherein the selecting and the presenting steps are based
at least in part on the second set of valuations. The request for the
placement may be a time limited request.

[0138] In embodiments, the economic valuation model may evaluate
performance information relating to each of the plurality of
advertisement placements.

[0139] In embodiments, a dynamically variable economic valuation model may
be used to determine the anticipated economic valuation. The dynamically
variable economic valuation model may evaluate bid values in relation to
economic valuations for a plurality of placements. A step of bidding for
at least one of the plurality of available placements, and/or plurality
of advertisements, may be based on the economic valuation.

[0140] Referring still to FIG. 2, the real-time bidding system may contain
an algorithm fitting the description above 200. Given a plurality of
possible ads to show the real-time bidding system may follow the
following exemplary sequence: 1) All possible ads may be filtered to show
using targeting rules, and an output a listed ads may be shown; 2) the
system may check if possible ads have remaining budget funds, and may
remove those ads that do not have available budget funds from the list;
3) the system may run an economic valuation dynamic algorithm for the ads
in order to determine the economic value for each ad; 4) the values may
be adjusted by the opportunity cost of placing an ad on a given site,
instead of alternative sites. 5) the ad with the highest value may be
selected, after adjusting by the opportunity cost; 6) Information about
the first request, which may include information about the publisher
content of a request, may be used to update the dynamic algorithm before
the second request is received and processed. This information may be
used to determine whether or not a particular type of publisher content
is available frequently or infrequently, and 7) the second ad may be
processed in the same sequence as the first, with the updates to the
dynamic algorithm before the third ad is placed.

[0141] In embodiments, the dynamic algorithm may be analogous to an
algorithm used in airplane flight control systems, which adjust for
atmospheric conditions as they change, or an automobile cruise control
system, which dynamically adjusts the gas pedal positions as wind drag
changes or the automobile climbs or descends a hill.

[0142] Referring to FIG. 3, data relating to context, the consumer (i.e.,
the digital media user), and the message/advertisement may be used to
predict the success of an advertisement based at least in part on
specified key performance indicators 300. Contextual data may include
data relating to the type of media, the time of day or week, or some
other type of contextual data. Data relating to a consumer, or digital
media user, may include demographics, geographic data, and data relating
to consumer intent or behavior, or some other type of consumer data. Data
relating to the message and/or advertisement may include data associated
with the creative content of the message/advertisement, the intention or
call to action embodied in the message/advertisement, or some other type
of data.

[0143] As depicted in FIG. 4, the real-time bidding system may be used to
produce advertising campaign-specific models and algorithms that are
continuously produced, tested, and run using data associated with
campaign results (e.g., clickthroughs, conversions, transactions, and the
like) as they become available in real-time 400. In embodiments, multiple
models may be tested using preparatory datasets to design sample
advertising campaigns. The multiple models may be run against multiple
training algorithms that embody specified objectives, such as key
performance indicators. Advertising content that performs well against
the algorithms may be retained and presented to a plurality of digital
media users. Additional data may be collected based at least in part on
the interactions of the plurality of digital media users and the selected
advertising content, and this data may be used to optimize the algorithms
and select new or different advertising content for presentation to the
plurality of digital media users.

[0144] Still referring to FIG. 4, in embodiments, a computer program
product embodied in a computer readable medium that, when executing on
one or more computers, may deploy an economic valuation model that may be
refined through machine learning to evaluate information relating to a
plurality of available placements, and/or plurality of advertisements, to
predict an economic valuation for each of the plurality of placements
400. At least one of the plurality of available placements, and/or
plurality of advertisements, may be selected and presented to the
publisher based at least in part on the economic valuation.

[0145] In embodiments, data may be taken from various formats, including
but not limited to information that is not about advertisements, such as
successful market demographics data, and the like. This may include
specific data streams, translating data into a neutral format, specific
machine learning techniques, or some other data type or technique. In
embodiments, the learning system may perform an auditing and/or
supervisory function, including but not limited to optimizing the methods
and systems as described herein. In embodiments, the learning system may
learn from multiple data sources, and base optimization of the methods
and systems as described herein based at least in part on the multiple
data sources.

[0146] In embodiments, the methods and systems as described herein may be
used in Internet-based applications, mobile applications, fixed-line
applications (e.g., cable media), or some other type of digital
application.

[0147] In embodiments, the methods and systems as described herein may be
used in a plurality of addressable advertising media, including but not
limited to set top boxes, digital billboards, radio ads, or some other
type of addressable advertising media.

[0148] Examples of machine learning algorithms may include, but are not
limited to, Naive Bayes, Bayes Net, Support Vector Machines, Logistic
Regression, Neural Networks, and Decision Trees. These algorithms may be
used to produce classifiers, which are algorithms that classify whether
or not an advertisement is likely to produce an action or not. In their
basic form, they return a "yes" or "no" answer and a score indicated the
strength of certainty of the classifier. When calibration techniques are
applied, they return a probability estimate of the likelihood of a
prediction to be correct. They can also return what specific advertising
is most likely to produce an action or which characteristics describe
advertisings most likely to produce an action. These characteristics can
include advertisings concept, advertisings size, advertisings color,
advertisings text, or any other characteristic of an advertisement.
Furthermore, they can also return what version of the advertiser website
is most likely to create an action or what characteristics describe the
version of the advertiser website most likely to produce an action. These
characteristics can include website concept, products presented, colors,
images, prices, text, or any other characteristic of the website. In
embodiments, a computer implemented method of the present invention may
comprise applying a plurality of algorithms to predict performance of
online advertising placements, and tracking performance of the plurality
of algorithms under a variety of market conditions. Preferred performance
conditions for a type of algorithm may be determined, and market
conditions tracked, and an algorithm may be selected for predicting
performance of advertising placements based at least in part on current
market conditions. In embodiments, the plurality of algorithms may
include three algorithms.

[0149] In embodiments, a computer program product embodied in a computer
readable medium that, when executing on one or more computers, may
predict, using a primary model, the economic valuation of each of a
plurality of available web publishable advertisement placements based in
part on past performance and prices of similar advertisement placements.
The economic valuation of each of the plurality of web publishable
advertisement placements may be predicted, through a second model, and
the valuations produced by the primary model and the second model may be
compared to determine a preference between the primary model and the
second model. In embodiments, the primary model may be an active model
responding to purchase requests. The purchase requested may be a time
limited purchase request. In embodiments, the second model may replace
the primary model as the active model responding to purchase requests.
The replacement may be based at least in part on a prediction that the
second model will perform better than the primary model under the current
market conditions.

[0150] In embodiments, a computer implemented method of the present
invention may apply a plurality of algorithms to predict performance of
online advertising placements, track performance of the plurality of
algorithms under a variety of market conditions, and determine preferred
performance conditions for a type of algorithm. Market conditions may be
tracked, and an algorithm for predicting performance of advertising
placements may be refined based at least in part on current market
conditions.

[0151] In embodiments, a computer implemented method of the present
invention may monitor a set of algorithms that are each predicting
purchase price value of a set of advertisements and selecting the best
algorithm from the set of algorithms based at least in part on a current
market condition.

[0152] Referring again to FIG. 4, new data may be entered into a sorting
mechanism (depicted by a funnel in FIG. 4) 400. This data may be prepared
for machine learning training by labeling each ad impression with an
indicator of whether or not it leads to a click or action. Alternative
machine learning algorithms may be trained on the labeled data. A portion
of the labeled may be saved for a testing phase. This testing portion may
be used to measure the prediction performance of each alternative
algorithm. Algorithms which are most successful in predicting the outcome
of the hold-out training data set may be forwarded to the real-time
decision system.

[0153] In embodiments, a computer program product embodied in a computer
readable medium that, when executing on one or more computers, may deploy
a plurality of competing economic valuation models, in response to
receiving to place an advertisement for a publisher, to predict an
economic valuation for each of the plurality of advertisement placements.
The valuations produced by each of the plurality of competing economic
valuation models may be evaluated to select one of the models for a
current valuation of an advertising placement. It will be understood that
general analytic methods, statistical techniques, and tools for
evaluating competing algorithms and models, such as valuation models, as
well as analytic methods, statistical techniques, and tools known to a
person of ordinary skill in the art are intended to be encompassed by the
present invention and may be used to evaluate competing algorithms and
valuation models in accordance with the methods and systems of the
present invention.

[0154] In embodiments, a computer program product embodied in a computer
readable medium that, when executing on one or more computers, may deploy
a plurality of competing economic valuation models, in response to
receiving a request to place an advertisement, to evaluate information
relating to a plurality of available advertisement placements. The
economic valuation models may be used to predict an economic valuation
for each of the plurality of advertisement placements. The valuations
produced by each of the plurality of competing economic valuation models
may be evaluated to select one of the models for future valuations. It
will be understood that general analytic methods, statistical techniques,
and tools for evaluating competing algorithms and models, such as
valuation models, as well as analytic methods, statistical techniques,
and tools known to a person of ordinary skill in the art are intended to
be encompassed by the present invention and may be used to evaluate
competing algorithms and valuation models in accordance with the methods
and systems of the present invention.

[0155] In embodiments, data may be evaluated to determine if it supports a
winning algorithm in a learning system. The incremental value of buying
additional data may be determined and auditing and testing of data
samples may be used to determine whether the data increases the
effectiveness of prediction. For example, the system may use data derived
from an ad server log, combined with demographical information, to derive
a valuation model, with a certain level of accuracy. Such a model may
enable the acquisition of online advertising ads, for the benefit of an
appliance manufacturer, below the market price. The addition of an
additional data source, such as a list of consumers that have expressed
their interest in buying a specific appliance, may increase the accuracy
of the model, and as a consequence the benefit to the appliance
manufacturer. It is stated that the increased benefit received would be
linked to the addition of the new data source, and hence, such data
source may be assigned a value linked to the incremental benefit.
Although this example presents a case of online advertising, it should be
appreciated by one skilled in the art that the application can be
generalized to advertising through different channels, using data sources
of different types, as well as models to predict economic value or
pricing for advertising.

[0156] As depicted in FIGS. 5A and 5B, an advertisement inventory may be
divided into many segments, or micro-segments (500, 502). The real-time
bidding system may produce and continuously revise algorithms, for
example by using the learning machine, based at least in part on data
received on the performance of the advertisements in the inventory and
its micro-segments (e.g., the number of impressions or conversions
associated with each advertisement). Based at least in part on the
learning system's algorithms, the real-time bidding system may produce a
bid value that is thought to be "fair" relative to the advertising
performance data. This bid value data may, in turn, be used to determine
an average bid value to associate with advertisements located in the
inventory. In embodiments, each micro-segment may be associated with a
rule, algorithm, or set of rules and/or algorithms, a price-to-paid,
and/or a budget. Rules may be used to buy advertising placement
opportunities in groups of one or more opportunities. The size of the
group of placement opportunities may be determined by the budget
allocated to the rule. Rules may be transmitted to sellers of advertising
placement opportunities through a server-to-server interface, through
other electronic communication channel, including phone and fax, through
a paper based order, through a verbal communication or any other way to
convey an order to buy advertising placement opportunities. FIG. 5C
depicts the use of frequency analysis for the purpose of pricing
optimization 504. FIG. 5D depicts how pacing may be optimized through
recency analysis within the real-time bidding system 508. Referring now
to FIG. 6, the real-time bidding system may enable the automated analysis
of an advertising inventory down to a nano-segment level (e.g., a bidding
value for each impression) in order to identify valuable segments (i.e.,
advertisements) of an otherwise low-value advertisement inventory 600.
The real-time bidding system may produce and continuously revise
algorithms, for example by using the learning machine, based at least in
part on data received on the performance of the advertisements in the
nano-segment of the advertising inventory (e.g., the number of
impressions associated with each advertisement). Based at least in part
on the learning system's algorithms, the real-time bidding system may
produce a bid value that is thought to be "fair" relative to the
advertisement(s) in the nano-segment, based at least in part on the
performance data. In embodiments, the average bid price associated with
the nano-segment may be adjusted based on other criteria, for example the
number of impressions associated with the advertisement. In embodiments,
each nano-segment may be associated with a rule, algorithm, or set of
rules and/or algorithms.

[0157] In embodiments, a computer program product embodied in a computer
readable medium that, when executing on one or more computers, may
predict a purchase price for each of a plurality of available web
publishable advertisement placements based at least in part on
performance information and past bid prices for each of the plurality of
advertisement placements. The purchase price for each of the plurality of
advertisements may be tracked and predicted to determine a pricing trend.

[0158] In embodiments, the pricing trend may include a prediction of
whether the valuation is going to change in the future.

[0159] In embodiments, a computer program product embodied in a computer
readable medium that, when executing on one or more computers, may
predict an economic valuation for each of a plurality of available web
publishable advertisement placements based at least in part on
performance information and past bid prices for each of the plurality of
advertisement placements. Economic valuations for each of the plurality
of advertisements may be tracked and predicted to determine a pricing
trend.

[0160] In an example, the system may present bids for buying ads in an
auction, expecting a fraction of them to be successful, and be awarded
the ads for which it sends bids. As the system operates, the fraction of
bids that is successful might fall below the expected goal. Such behavior
can happen for the universe of available ads or for a subset of them. The
price trend predicting algorithm may estimate what correction should be
done to the bid price, so that, the fraction of ads successfully bought
becomes closer to the intended goal, and may finally reach the intended
goal.

[0161] As depicted in FIG. 7, the real-time bidding method and system as
described herein may be integrated, associated, and/or affiliated with a
plurality of organizations and organization types, including but not
limited to advertisers and advertising agencies 700. The real-time
bidding system may perform buy-side optimization using the learning
algorithms and techniques, as described herein, to optimize the selection
of advertisements from sell-side aggregators, such as sell-side
optimizers, ad networks, and/or exchanges, that receive advertisements
from content publishers. This may optimize the pairing of messages and
advertisements that are available within the inventories with digital
media users. Advertising agencies may include Internet-based advertising
companies, advertising sellers, such as organizations that sell
advertisement impressions that display to a digital media user, and/or
advertising buyers. Advertisers and advertising agencies may provide the
real-time bidding system advertising campaign descriptors. A campaign
descriptor may include, but is not limited to, a channel, time, budget,
or some other type of campaign descriptor data. In embodiments,
advertising agency data may include historic logs that describe the
placement of each advertisement and user impression, conversion, and the
like, including, but not limited to an identifier associated with a user,
a channel, time, price paid, advertisement shown, resulting user actions,
or some other type of historic data relating to the advertisement and/or
impression. Historic logs may also include data relating to spontaneous
user actions. In embodiments, advertiser data utilized by the real-time
bidding system may include, but is not limited to, metadata relating to
the subject matter of an advertisement, for example, inventory levels of
a product that is the subject of the advertisement. Valuation, bid
amounts, and the like may be optimized according to this and other
metadata. Valuation, bid amounts, and the like may be optimized according
to key performance indicators.

[0162] FIGS. 8A and 8B depict hypothetical case studies using a real-time
bidding method and system (800, 802). In embodiments, the learning system
may create rules and algorithms, as described herein, using training data
sets, such as that derived from a prior retailer advertising campaign.
The training dataset may include a record of prior impressions,
conversions, actions, clickthroughs and the like performed by a plurality
of digital media users with the advertisements that were included in the
prior campaign. The learning system may then identify a subset of
advertising content from the prior campaign that was relatively more
successful that other of the advertisements in the campaign, and
recommend this advertising content for future use on the basis of its
higher expected value.

[0163] In embodiments, a computer program product embodied in a computer
readable medium that, when executing on one or more computers, may deploy
an economic valuation model, in response to receiving a request to place
an advertisement, in order to evaluate information relating to a
plurality of available advertisement placements. The economic valuation
model may be used to predict an economic valuation or the pricing for
bids for each of the plurality of advertisement placements. A hypothesis
as to a market opportunity may be determined, and the economic valuation
model may be updated in response to the hypothesized market opportunity.

[0164] In an example, the system may find every few seconds, a data set or
identify changes to the model that improves the accuracy of the valuation
model used to predict economic value of ads. The system may have
limitations on its ability to replace the valuation model on its whole,
at the same rate as new data or changes to the model are created. As a
consequence it may be beneficial to select which parts are less effective
at providing economic valuation. The opportunistic updating component may
select what is the order and priority for replacing sections of the
valuation model. Such prioritization may be based on the economic
valuation of the section to replace versus the new section to
incorporate. As a result the system may create a prioritized set of
instructions as to what data or sections of the model to add to the
valuation system and in what order to do so.

[0165] In embodiments, the method and system of the present invention may
split an advertising campaign, and compare the performance of a first set
from the campaign using the methods and systems as described herein with
a second set from the campaign not using the methods and systems. The
analytic comparison may show the lift and charge based on the lift
between the first set and the second set (e.g., third party campaign).

[0166] In an example, the system may separate a fraction of ads for
creating a baseline sample on which the system is not applied, and thus,
its benefits may not be delivered. Such process may be automatic. Such
separation may be done by a random selection, across the universe of
available ads, or to a randomly selected panel of users. The remaining
ads that do not belong to the baseline sample may be placed using the
system.

[0167] In embodiments, as the ad campaign presents some objectives that
are possible to measure, and the greater the benefit, the better is the
campaign judged to be, it stands to believe an advertiser is willing to
pay a premium for ad campaigns that deliver increased benefits.

[0168] In embodiments, the pricing model may calculate the difference
between the benefit created by ads placed using the system and those
placed without the system, as on the baseline sample. The system benefit
is such net difference. The price charged to the advertiser may be a
fraction of the system benefit.

[0169] FIG. 9 depicts a simplified flow chart summarizing key steps that
may be involved in using a real-time bidding method and system 900.

[0170] FIG. 10 depicts an exemplary embodiment of a user interface for a
pixel provisioning system that may be associated with the real-time
bidding system 1000.

[0171]FIG. 11 depicts an exemplary embodiment of impression level data
that may be associated with the real-time bidding system 1100.

[0173] FIG. 13 illustrates a bidding valuation facility 1300 for real-time
bidding and valuation for purchases of online advertising placements in
accordance with an embodiment of the invention. The bidding valuation
facility 1300 may further include (apart from other facilities) a
publisher facility 112, an analytics platform facility 114, an
advertising order sending and receiving facility 120, a contextualizer
service facility 132 a data integration facility 134, one or more
databases providing different types of data for use by the analytics
facility. In an embodiment of the invention, the analytics platform
facility 114 may include a learning machine facility 138, valuation
algorithm facility 140, a real-time bidding machine facility 142, a
tracking machine facility 144, an Impression/Click/Action Logs facility
148, and a real-time bidding logs facility 150.

[0174] In embodiments of the invention, a learning machine 138 may be used
to develop targeting algorithms for the real-time bidding machine
facility 142. The learning machine 138 may learn patterns, including
social behavior and inferred demographics among others, which may be used
to target online ads. Further, the learning machine facility 138 may be
coupled to one or more databases. In embodiments of the invention, the
one or more databases may include an ad agency/advertiser database 152.
The ad agency data 152 may include campaign descriptors, and may describe
the channels, times, budgets, and other information that may be allowed
for diffusion of advertising messages. The ad agency data 152 may also
include campaign and historic logs that may be the placement for each
advertising message to be shown to the user. The ad agency data 152 may
include one or more of the following: an identifier for the user, the
channel, time, price paid, ad message shown, and user resulting user
actions, or some other type of campaign or historic log data. Further,
the advertiser data 152 may include business intelligence data, or some
other type of data, which may describe dynamic and/or static marketing
objectives. In an example, the amount of overstock of a given product
that the advertiser 104 has in its warehouses may be described by the
advertiser data 152. Further, the one or more databases may include an
historic event database. The historic event data 154 may be used to
correlate the time of user events with other events happening in their
region. In an example, response rates to certain types of advertisements
may be correlated to stock market movements. The historic event data 154
may include, but is not limited to, weather data, events data, local news
data, or some other type of data. Further, the one or more databases may
include a user database. The user data 158 may include data provided by
third parties that may contain personally linked information about
advertising recipients. This information may provide users with
preferences, or other indicators, which may label or describe the users.
Further, the one or more databases may include a real-time event
database. The real-time event data 160 may include data similar to
historic data, but that is more current. The real-time event data 160 may
include, but is not limited to, data that is current to the second,
minute, hour, day, or some other measure of time. In an example, if the
learning machine facility 138 finds a correlation between advertising
performance and historic stock market index values, the real-time stock
market index value may be used to value advertisements by the real-time
bidding machine facility 142. Further, the one or more databases may
include a contextual database that may provide contextual data 162
associated with a publisher 112, publisher's website and the like. The
one or more databases may further include a third party/commercial
database.

[0175] Further, in embodiments of the invention, a data integration
facility 134 and the contextualizer service facility 132 may be
associated with the analytics platform facility 114 and the one or more
databases. The data integration facility 134 may facilitate the
integration of different types of data from one or more databases into
the analytics platform facility 114. The contextualizer service facility
132 may identify the contextual category of a medium for advertising
and/or publisher content, website, or other publisher ad context. In an
example, a contextualizer may analyze web content to determine whether a
web page contains content about sports, finance, or some other topic.
This information may be used as an input to the learning machine facility
in order to identify the relevant publishers and/or web pages where ads
may appear. In another embodiment, the location of the ad on the
publisher 112 web page may be determined based on the information. In an
embodiment of the invention, the contextualizer service facility 132 may
also be associated with the real-time bidding machine facility 142 and/or
with the one or more databases.

[0176] In embodiments of the invention, the real-time bidding machine
facility 142 may receive a bid request message from the publisher
facility 112. A real-time bidding machine facility 142 may be considered
a "real-time" facility since it may reply to a bid request that is
associated with a time constraint, where the reply occurs substantially
simultaneous to the request receipt, and/or very near in time to the
request receipt. The real-time bidding machine facility 142 may use a
non-stateless method to calculate which advertising message to show,
while the user waits for the system to decide. The real-time bidding
machine facility 142 may perform the real-time calculation using
algorithms provided by the learning machine 138, dynamically estimating
an optimal bid value. In embodiments, an alternative real-time bidding
machine facility 142 may have a stateless configuration to determine an
advertisement to present.

[0177] Further, in an embodiment of the invention, the real-time bidding
machine facility 142 may dynamically determine an anticipated economic
valuation for each of the plurality of potential placements for an
advertisement based on receiving the request to place an advertisement
for the publisher facility 112. In response to receiving a request to
place an advertisement for the publisher facility 112, the real-time
bidding machine facility 142 may dynamically determine an anticipated
economic valuation for each of the plurality of potential placements for
the advertisement, and may select and decide whether to present the
available placements based on the economic valuation to the publisher
facility 112.

[0178] In embodiments, the real-time bidding machine facility 142 may
include altering a model for dynamically determining the economic
valuation prior to processing a second request for a placement. The
alteration of the model may be based at least in part on the machine
learning facility. In an embodiment of the invention, prior to selecting
and presenting at least one of the plurality of available placements,
and/or plurality of advertisements, the behavior of an economic valuation
model may be altered to produce a second set of valuations for each of
the plurality of placements. In embodiments, the steps for selecting and
presenting may be based on the second set of valuations. Further, in an
embodiment of the invention, the request for the placement may be a
time-limited request. Further, the economic valuation model may evaluate
performance information relating to each of the plurality of
advertisement placements. The dynamically variable economic valuation
model may also be used to determine an anticipated economic valuation. In
an embodiment of the invention, the dynamically variable economic
valuation model may evaluate bid values in relation to economic
valuations for a plurality of placements. Dynamic determination of an
anticipated economic valuation for each of the plurality of potential
placements for an advertisement may be based at least in part on
advertiser data 152, historical event data 154, user data 158, real-time
event data 160, contextual data 162, and third-party commercial data 164.

[0179] In embodiments, the real-time bidding machine facility 142, in
response to receiving a request to place an advertisement for a publisher
112, may dynamically determine an anticipated economic valuation for each
of a plurality of potential placements for an advertisement. After the
economic valuation model has been determined, the real-time bidding
machine facility 142 may determine a bid amount based at least in part on
the anticipated economic valuation for each of the plurality of potential
placements for the advertisement. The determination of the bid amount may
include analysis of real-time bidding logs. In another embodiment, the
determination of the bid amount may include analytic modeling based at
least in part on machine learning. Analytic modeling based at least in
part on machine learning may include the analysis of historical log data
summarizing at least one of: ad impressions, ad clickthroughs, and user
actions taken in association with an ad presentation. Further, in an
embodiment of the invention, the determination of the bid amount may
include analysis of data from the contextualizer service facility 132.

[0180] In an embodiment of the invention, the real-time bidding machine
facility 142, in response to receiving a request to place an
advertisement for a publisher 112, may dynamically determine an
anticipated economic valuation for each of a plurality of potential
placements for the advertisement. After the economic valuation model has
been determined, the real-time bidding machine facility 142 may determine
a bid amount based at least in part on the anticipated economic valuation
for each of the plurality of potential placements for the advertisement.
Thereafter, the real-time bidding machine facility may select an optimum
placement for the advertisement, from among the plurality of potential
placements. Further, the real-time bidding machine facility 142 may
automatically place a bid on the optimum placement for the advertisement.

[0181]FIG. 14 illustrates a method 1400 for selecting and presenting to a
publisher at least one of the plurality of available placements, and/or
plurality of advertisements, based on an economic valuation. The method
initiates at step 1402. At step 1404, in response to receiving a request
to place an advertisement for a publisher, an anticipated economic
valuation may be dynamically determined for each of a plurality of
potential placements for the advertisement. Thereafter at step 1408, at
least one of the plurality of available placements, and/or plurality of
advertisements, may be selected and presented to the publisher based at
least in part on the economic valuation. In an embodiment of the
invention, a model for dynamically determining the economic valuation may
be altered prior to processing a second request for a placement. In an
embodiment the model may be altered based at least in part on machine
learning. In an embodiment of the invention, prior to the steps of
selecting and presenting, the behavior of an economic valuation model may
be altered to produce a second set of valuations for each of the
plurality of placements. In an embodiment, the steps of selecting and
presenting steps may be based on the second set of valuations, which are
used in place of the first valuation (s). In embodiments, the request for
the placement may be a time limited request. In embodiments, the economic
valuation model, as described herein, may evaluate performance
information relating to each of a plurality of advertisement placements.
A dynamically variable economic valuation model may be used to determine
the anticipated economic valuation and to evaluate bid values in relation
to economic valuations for a plurality of placements. An anticipated
economic valuation for each of a plurality of potential placements for an
advertisement may be based at least in part on advertiser data,
historical event data, user data, real-time event data, contextual data
or third-party commercial data. The method terminates at step 1410.

[0182] FIG. 15 illustrates a method 1500 for determining a bid amount, in
accordance with an embodiment of the invention. The method initiates at
step 1502. At step 1504, in response to receiving a request to place an
advertisement for a publisher, an anticipated economic valuation for each
of a plurality of potential placements for the advertisement may be
dynamically determined. Thereafter at step 1508, a bid amount based at
least in part on the anticipated economic valuation for each of the
plurality of potential placements for the advertisement is determined. In
an embodiment of the invention, the determination of the bid amount may
include analysis of real-time bidding logs and/or analytic modeling based
at least in part on machine learning. In an embodiment of the invention,
the analytic modeling may include the analysis of historical log data
summarizing at least one of: ad impressions, ad clickthroughs, and user
actions taken in association with an ad presentation. In an embodiment of
the invention, determination of the bid amount may include analysis of
data from a contextualizer service.

[0183]FIG. 16 illustrates a method 1600 for automatically placing a bid
on an optimum placement for an advertisement, where the optimum placement
is selected based at least in part on an anticipated economic valuation.
The method initiates at step 1602. At step 1604, in response to receiving
a request to place an advertisement for a publisher, an anticipated
economic valuation for each of a plurality of potential placements for
the advertisement is dynamically determined. Thereafter at step 1608, a
bid amount based at least in part on the anticipated economic valuation
for each of the plurality of potential placements for the advertisement
is determined. Further at step 1610, an optimum placement for the
advertisement is selected, from among the plurality of potential
placements, based at least in part on the bid amount. Finally at step
1612, a bid on the optimum placement for the advertisement is
automatically placed. The method terminates at step 1614.

[0184]FIG. 17 illustrates a real-time facility 1700 for targeting bids
for online advertising purchases in accordance with an embodiment of the
invention. The real-time facility may include a learning machine facility
138 and a real-time bidding machine facility 142. In an embodiment of the
invention, the real-time bidding machine facility 142 may receive a bid
request message from the publisher facility 112. The real-time bidding
machine facility 142 may be considered a "real-time" facility since it
may reply to a bid request that is associated with a time constraint. The
real-time bidding machine facility 142 may perform the real-time
calculation using targeting algorithms provided by the learning machine
138, dynamically estimating an optimal bid value.

[0185] Further, in an embodiment of the invention, the real-time bidding
machine facility 142 may deploy an economic valuation model that may
dynamically determine an economic valuation (based on receiving the
request to place an advertisement for the publisher facility 112) for
each of one or more potential placements for an advertisement. In
response to receiving a request to place an advertisement for the
publisher facility 112, the real-time bidding machine facility 142 may
dynamically determine an economic valuation for each of one or more
potential placements for the advertisement. After the economic valuation
has been determined, the real-time bidding machine facility 142 may
select and present to a user at least one of the plurality of available
placements, and/or plurality of advertisements, based on the economic
valuation. In an embodiment, the selection and presentation to the
publisher 112 may include a recommended bid amount for the at least one
of the plurality of available placements, and/or plurality of
advertisements. The bid amount may be associated with a time constraint.
Further, in an embodiment, the refinement through machine learning may
include comparing economic valuation models by retrospectively comparing
the extent to which the models reflect actual economic performance of
advertisements. In embodiments of the invention, the economic valuation
model may be based at least in part on advertising agency data 152,
real-time event data 160, historic event data 154, user data 158, third
party commercial data 164, and contextual data 162. In an embodiment, the
advertising agency data 152 may include at least one campaign descriptor.
In embodiments, the campaign descriptor may be historic log data,
advertising agency campaign budget data, and a datum indicating a
temporal restraint on an advertising placement.

[0186] In embodiments, the learning machine facility 138 may receive an
economic valuation model. The economic valuation model may be based at
least in part on analysis of real-time bidding log data 150 from the real
time bidding machine facility 142. Thereafter, the learning machine
facility 138 may refine the economic valuation model. The refinement may
be based at least in part on analysis of an advertising impression log.
In an embodiment of the invention, the refinement of the economic
valuation model may include a data integration step during which data to
be used in the learning machine facility 138 may be transformed into a
data format that may be read by the learning machine facility 138. The
format may be a neutral format. Further in embodiments, the refinement of
the economic valuation model using the learning machine may be based at
least in part on a machine learning algorithm. The machine learning
algorithms may be based at least in part on naive bayes analytic
techniques and on logistic regression analytic techniques. Further, the
real-time bidding machine facility 142 may use the refined economic
valuation model to classify each of a plurality of available advertising
placements. The classification may be a datum indicating a probability of
each of the available advertising placements achieving an advertising
impression. The real-time bidding machine facility 142 may then
prioritize the available advertising placements based at least in part on
the datum indicating the probability of achieving an advertising
impression. Thereafter, the real-time bidding machine facility 142 may
select and present to a user at least one of the plurality of available
placements, and/or plurality of advertisements, based on the
prioritization.

[0187] In an embodiment of the invention, an economic valuation model
deployed by the real-time bidding machine facility 142 may be refined by
the machine learning facility to evaluate information relating to one or
more available placements to predict an economic valuation for each of
the one or more placements. Further, in embodiments, the learning machine
facility 138 may obtain different types of data to refine the economic
valuation model. The different types of data may include, without any
limitation, agency data 152 which may include campaign descriptors, and
may describe the channels, times, budgets, and other information that may
be allowed for diffusion of advertising messages. The agency data 152 may
also include campaign and historic logs that may be the placement for
each advertising message to be shown to the user. The agency data 152 may
also include one or more of the following: an identifier for the user,
the channel, time, price paid, ad message shown, and user resulting user
actions, or some other type of campaign or historic log data. Further,
the different types of data may include business intelligence data, or
some other type of data, which may describe dynamic and/or static
marketing objectives.

[0188] In embodiments of the invention, the learning machine facility 138
may perform an auditing and/or supervisory function, including, but not
limited to, optimizing the methods and systems as described herein. In
other embodiments of the information, the learning system 138 may learn
from multiple data sources, and base optimization of the methods and
systems as described herein at least in part on the multiple data
sources. In embodiments, the methods and systems as described herein may
be used in Internet-based applications, mobile applications, fixed-line
applications (e.g., cable media), or some other type of digital
application. In embodiments, the methods and systems as described herein
may be used in one or more addressable advertising media, including, but
not limited to, set top boxes, digital billboards, radio ads, or some
other type of addressable advertising media.

[0189] Further, in embodiments of the invention, the learning machine
facility 138 may utilize various types of algorithms to refine the
economic valuation models of the real-time bidding machine facility 142.
The algorithms may include, without any limitations, decision tree
learning, association rule learning, artificial neural networks, genetic
programming, inductive logic programming, support vector machines,
clustering, Bayesian networks, and reinforcement learning. In an
embodiment of the invention, the various types of algorithms may produce
classifiers, which are algorithms that may classify whether or not an
advertisement is likely to produce an action. In their basic form, they
may return a "yes" or "no" answer and/or a score indicating the strength
of certainty of the classifier. When calibration techniques are applied,
they may return a probability estimate of the likelihood of a prediction
to be correct.

[0190]FIG. 18 illustrates a method 1800 for selecting and presenting to a
user at least one of a plurality of available advertising placements
based on an economic valuation. The method initiates at step 1802. At
step 1804, an economic valuation model may be deployed, in response to
receiving a request to place an advertisement for a publisher. The
economic valuation model may be refined through machine learning to
evaluate information relating to a plurality of available placements,
and/or plurality of advertisements, to predict an economic valuation for
each of the plurality of placements. In an embodiment, the refinement
through machine learning may include comparing economic valuation models
by retrospectively comparing the extent to which the models reflect
actual economic performance of advertisements. Further, the economic
valuation model may be based at least in part on advertising agency data,
real time event data, historic event data, user data, third-party
commercial data and contextual data. Furthermore, the advertising agency
data may include at least one campaign descriptor. Moreover, the campaign
descriptor may be historic log data, is advertising agency campaign
budget data and advertising agency campaign budget data. At step 1808, at
least one of the plurality of available placements, and/or plurality of
advertisements, based on the economic valuation may be selected and
presented to a user. In an embodiment, the selection and presentation to
the publisher may include a recommended bid amount for the at least one
of the plurality of available placements, and/or plurality of
advertisements. Further, the bid amount may be associated with a time
constraint. The method 1800 terminates at step 1810.

[0191] FIG. 19 illustrates a method 1900 for selecting from a plurality of
available advertising placements a prioritized placement opportunity
based at least in part on an economic valuation model using real-time
bidding log data. The method 1900 initiates at step 1902. At step 1904,
an economic valuation model at a learning machine may be received. The
economic valuation model may be based at least in part on analysis of a
real-time bidding log from a real time bidding machine. At step 1908, the
economic valuation model may be refined using the learning machine. In an
embodiment, the refinement may be based at least in part on analysis of
an advertising impression log. Further, the refinement of the economic
valuation model may include a data integration step during which data to
be used in the learning machine may be transformed into a data format
that can be read by the learning machine. In an embodiment, the format
may be a neural format. Furthermore, the refinement of the economic
valuation model using the learning machine may be based at least in part
on a machine learning algorithm. The machine learning algorithm may be
based at least in part on naive bayes analytic techniques. Moreover, the
machine learning algorithm may be based at least in part on logistic
regression analytic techniques. At step 1910, the refined economic
valuation model may be used to classify each of a plurality of available
advertising placements. Each classification may be a summarized using a
datum indicating a probability of each of the available advertising
placements achieving an advertising impression. Further, at step 1912,
the available advertising placements may be prioritized based at least in
part on the datum. In addition, at step 1914, at least one of the
plurality of available placements, and/or plurality of advertisements,
may be selected and presented to a user based on the prioritization. The
method 1900 terminates at step 1918.

[0192] FIG. 20 illustrates a real-time facility 2000 for selecting
alternative algorithms for predicting purchase price trends for bids for
online advertising, in accordance with an embodiment of the invention.
The real-time facility 1700 may include a learning machine facility 138,
a valuation algorithm facility 140, a real-time bidding machine facility
142, a plurality of data 2002, and a bid request message 2004 from a
publisher facility 112. In an embodiment of the invention, the real-time
bidding machine facility 142 may receive a bid request message 1704 from
the publisher facility 112. The real-time bidding machine facility 142
may be considered a "real-time" facility since it may reply to a bid
request that is associated with time constraint. The real-time bidding
machine facility 142 may perform a real-time calculation using targeting
algorithms provided by the learning machine facility 138 to predict
purchase price trends for bids for online advertising. In an embodiment
of the invention, the learning machine facility 138 may select an
alternative algorithm based on the performance of a currently operating
algorithm for predicting purchase price trends for bids for online
advertising.

[0193] In another embodiment of the invention, the learning machine
facility 138 may select an alternative algorithm based on the predicted
performance of the alternative algorithm for predicting purchase price
trends for bids for online advertising. Further, in an embodiment of the
invention, learning machine facility 138 may obtain the alternative
algorithms from the valuation algorithm facility 140.

[0194] In embodiments, the real-time bidding machine facility 142 may
apply a plurality of algorithms to predict performance of online
advertising placements. Once the plurality of algorithms is applied, the
real-time bidding machine facility 142 may track the performance of the
plurality of algorithms under a variety of market conditions. The
real-time bidding machine facility 142 may then determine the performance
conditions for a type of algorithm from the plurality of algorithms.
Thereafter, the real-time bidding machine facility 142 may track the
market conditions and may select the algorithm for predicting performance
of advertising placements based on the current market conditions.

[0195] In embodiments, at least one of the plurality of algorithms to
predict performance may include advertiser data 152. The advertiser data
152 my include business intelligence data, or some other type of data,
which may describe dynamic and/or static marketing objectives. In another
embodiment of the invention, at least one of the plurality of algorithms
to predict performance may include historic event data 154. The historic
event data 154 may be used to correlate the time of user events with the
occurrence of other events in their region. In an example, response rates
to certain types of advertisements may be correlated to stock market
movements. The historic event data 154 may include, but is not limited
to, weather data, events data, local news data, or some other type of
data. In yet another embodiment of the invention, at least one of the
plurality of algorithms to predict performance may include user data 158.
The user data 158 may include data provided by third parties, which may
contain personally linked information about advertising recipients. This
information may provide users with preferences, or other indicators,
which may label or describe the users. In yet another embodiment of the
invention, at least one of the plurality of algorithms to predict
performance may include real-time event data 160. The real-time event
data 160 may include data similar to historic data, but more current. The
real-time event data 160 may include, but is not limited to, data that is
current to the second, minute, hour, day, or some other measure of time.
In yet another embodiment of the invention, at least one of the plurality
of algorithms to predict performance may include contextual data 162. In
yet another embodiment of the invention, at least one of the plurality of
algorithms to predict performance may include third party commercial
data.

[0196] Further, in an embodiment of the invention, the real-time bidding
machine facility 142 may use a primary model for predicting an economic
valuation of each of a plurality of available web publishable
advertisement placements based in part on past performance and prices of
similar advertisement placements. The real-time bidding machine facility
142 may also use a second model for predicting an economic valuation of
each of the plurality of web publishable advertisement placements. After
predicting the economic valuations using both the primary model and the
second model, the real-time bidding machine facility 142 may compare the
valuations produced by the primary model and the second model to
determine a preference between the primary model and the second model. In
an embodiment of the invention, the comparison of the valuations may
include retrospectively comparing the extent to which the models reflect
actual economic performance of advertisements. Further, in an embodiment
of the invention, the primary model may be an active model responding to
purchase requests. The purchase request may be a time limited purchase
request. In an embodiment of the invention, the second model may replace
the primary model as the active model responding to purchase requests.
Further, the replacement may be based on a prediction that the second
model may perform better than the primary model under the current market
conditions. In embodiments of the invention, the prediction may be based
at least in parts on machine learning, historical advertising performance
data 130, historical event data, and real-time event data 160.

[0197] In another embodiment of the invention, the real-time bidding
machine facility 142 may use a primary model for predicting an economic
valuation of each of a plurality of available mobile device advertisement
placements based in part on past performance and prices of similar
advertisement placements. The real-time bidding machine facility 142 may
also use a second model for predicting an economic valuation of each of
the plurality of mobile device advertisement placements. After predicting
the economic valuations using both the primary model and the second
model, the real-time bidding machine facility 142 may compare the
valuations produced by the primary model and the second model to
determine a preference between the primary model and the second model. In
an embodiment of the invention, the comparison of the valuations may
include retrospectively comparing the extent to which the models reflect
actual economic performance of advertisements. Further, in an embodiment
of the invention, the primary model may be an active model responding to
purchase requests. The purchase request may be a time limited purchase
request. In an embodiment of the invention, the second model may replace
the primary model as the active model responding to purchase requests.
Further, the replacement may be based on a prediction that the second
model may perform better than the primary model under the current market
conditions.

[0198] In an embodiment of the invention, the economic valuation model
deployed by the real-time bidding machine facility 142 may be refined by
the machine learning facility 138 to evaluate information relating to one
or more available placements to predict an economic valuation for each of
the one or more placements.

[0199] In embodiments, the learning machine facility 138 may obtain
different types of data to refine the economic valuation model. The
different types of data may include, without any limitation, advertiser
data 152, historic event data 154, user data 158, real-time event data
160, contextual data 162, and third party commercial data. The different
types of data may have different formats and information that may not
directly relate to the advertisements, such as market demographics data,
and the like. In embodiments of the invention, the different types of
data in different formats may be translated into a neutral format or
specific to a format compatible with the learning machine facility 138,
or some other data type suitable for the learning machine facility 138.

[0201] FIG. 21 illustrates a method 2100 of the present invention for
predicting performance of advertising placements based on current market
conditions. The method initiates at step 2102. At step 2104, a plurality
of algorithms to predict performance of online advertising placement may
be applied. In embodiments of the invention, at least one of the
plurality of algorithms to predict performance may include advertiser
data, historic event data, user data, real-time event data, contextual
data, and third-party commercial data, of some other type of data.
Thereafter, at step 2108, the performance of the plurality of algorithms
may be tracked under various market conditions. Further, at step 2110,
the performance for a type of algorithm may be determined and then the
market conditions may be tracked at step 2112. Finally, at step 2114, an
algorithm for predicting performance of advertising placements based on
the current market conditions may be selected. The method terminates at
step 2118.

[0202]FIG. 22 illustrates a method 2200 for determining a preference
between a primary model and a second model for predicting an economic
valuation, in accordance with an embodiment of the invention. The method
initiates at step 2202. At step 2204, using a primary model, an economic
valuation of each of a plurality of available web publishable
advertisement placements may be predicted. The economic valuation may be
based in part on past performance and prices of similar advertisement
placements. At step 2208, using a second model, an economic valuation of
each of the plurality of available web publishable advertisement
placements may be predicted. Thereafter, at step 2210, the economic
valuations using both the primary model and the second model may be
compared to determine a preference between the primary model and the
second model. In an embodiment of the invention, the comparison of the
valuations may include retrospectively comparing the extent to which the
models reflect actual economic performance of advertisements. Further, in
an embodiment of the invention, the primary model may be an active model
responding to purchase requests. The purchase request may be a time
limited purchase request. In an embodiment of the invention, the second
model may replace the primary model as the active model responding to
purchase requests. Further, the replacement may be based on a prediction
that the second model may perform better than the primary model under the
current market conditions. In embodiments of the invention, the
prediction may be based at least in parts on machine learning, historical
advertising performance data, historical event data, and real-time event
data. The method terminates at step 2212.

[0203] Referring now to FIG. 23, which illustrates a method 2300 for
determining a preference between a primary model and a second model for
predicting economic valuation, in accordance with another embodiment of
the invention. The method initiates at step 2302. At step 2304, using a
primary model, an economic valuation of each of a plurality of available
mobile device advertisement placements may be predicted. The economic
valuation may be based in part on past performance and prices of similar
advertisement placements. At step 2308, using a second model an economic
valuation of each of the plurality of available mobile device
advertisement placements may be predicted. Thereafter, at step 2310, the
economic valuations using both the primary model and the second model may
be compared to determine a preference between the primary model and the
second model. In an embodiment of the invention, the comparison of the
valuations may include retrospectively comparing the extent to which the
models reflect actual economic performance of advertisements. Further, in
an embodiment of the invention, the primary model may be an active model
responding to purchase requests. The purchase request may be a time
limited purchase request. In an embodiment of the invention, the second
model may replace the primary model as the active model responding to
purchase requests. Further, the replacement may be based on a prediction
that the second model may perform better than the primary model under the
current market conditions. The method terminates at step 2312.

[0204] Further in an embodiment of the invention, the real-time bidding
machine facility 142 may receive a request to place an advertisement from
a publisher facility 112. In response to this request, the real-time
bidding machine facility 142 may deploy a plurality of competing economic
valuation models to predict an economic valuation for each of a plurality
of available advertisement placements. After deploying the plurality of
economic valuation models, the real-time bidding machine facility 142 may
evaluate each valuation produced by each of the plurality of competing
economic valuation models to select one economic valuation model as a
current valuation of an advertising placement.

[0205] In an embodiment of the invention, the economic valuation model may
be based at least in part on real-time event data 160. The real-time
event data 160 may include data similar to historic data, but more
current. The real-time event data 160 may include, but is not limited to,
data that is current to the second, minute, hour, day, or some other
measure of time. In another embodiment of the invention, the economic
valuation model may be based at least in part on historic event data 154.
The historic event data 154 may be used to correlate the time of user
events with the occurrence of other events in their region. In an
example, response rates to certain types of advertisements may be
correlated to stock market movements. The historic event data 154 may
include, but is not limited to, weather data, events data, local news
data, or some other type of data. In yet another embodiment of the
invention, the economic valuation model may be based at least in part on
the user data 158. The user data 158 may include data provided by third
parties, which may contain personally linked information about
advertising recipients. This information may provide users with
preferences, or other indicators, which may label or describe the users.
In yet another embodiment of the invention, the economic valuation model
may be based at least in part on the third party commercial data. In an
embodiment of the invention, the third party commercial data may include
financial data relating to historical advertisement impressions. In yet
another embodiment of the invention, the economic valuation model may be
based at least in part on contextual data 162. In yet another embodiment
of the invention, the economic valuation model may be based at least in
part on advertiser data 152. The advertiser data 152 may include business
intelligence data, or some other type of data, which may describe dynamic
and/or static marketing objectives. In yet another embodiment of the
invention, the economic valuation model may be based at least in part on
ad agency data 152. The ad agency data 152 may also include campaign and
historic logs that may be the placement for each advertising message to
be shown to the user. The ad agency data 152 may also include one or more
of the following: an identifier for the user, the channel, time, price
paid, ad message shown, and user resulting user actions, or some other
type of campaign or historic log data. In yet another embodiment of the
invention, the economic valuation model may be based at least in part on
the historical advertising performance data 130. In yet another
embodiment of the invention, the economic valuation model may be based at
least in part on the machine learning.

[0206] In an embodiment of the invention, an economic valuation model
deployed by the real-time bidding machine facility 142 may be refined by
the machine learning facility 138 to evaluate information relating to one
or more available placements to predict an economic valuation for each of
the one or more placements.

[0207] In an embodiment of the present invention, after the real-time
bidding machine facility 142 receives a request to place an advertisement
from a publisher facility 112, the real-time bidding machine facility 142
in response to this request may deploy a plurality of competing economic
valuation models to predict an economic valuation for each of the
plurality of advertisement placements. After deploying the plurality of
economic valuation models, the real-time bidding machine facility 142 may
evaluate each valuation produced by each of the plurality of competing
economic valuation models to select one as a first valuation of an
advertising placement. Upon selecting the first valuation, the real-time
bidding machine facility 142 may reevaluate each valuation produced by
each of the plurality of competing economic valuation models to select
one as a revised valuation of an advertising placement. In an embodiment
of the invention, the revised valuation may be based at least in part on
analysis of an economic valuation model using real-time event data 160
that was not available at the time of selecting the first valuation.
Thereafter, real-time bidding machine facility 142 may replace the first
valuation by the second revised valuation for use in deriving a
recommended bid amount for the advertising placement. In an embodiment of
the invention, the request may be received from a publisher 112 and the
recommended bid amount may be automatically sent to the publisher 112. In
another embodiment of the invention, the request may be received from a
publisher 112 and a bid equaling the recommended bid amount may be
automatically placed on behalf of the publisher 112. In an embodiment of
the invention, the recommended bid amount may be associated with a
recommended time of ad placement. In another embodiment of the invention,
the recommended bid amount may be further derived by analysis of a
real-time bidding log that may be associated with a real-time bidding
machine facility 142. It will be understood that general analytic
methods, statistical techniques, and tools for evaluating competing
algorithms and models, such as valuation models, as well as analytic
methods, statistical techniques, and tools known to a person of ordinary
skill in the art are intended to be encompassed by the present invention
and may be used to evaluate competing algorithms and valuation models in
accordance with the methods and systems of the present invention.

[0208] In another embodiment of the invention, after the real-time bidding
machine facility 142 receives a request to place an advertisement from a
publisher facility 112, the real-time bidding machine facility 142 may
deploy a plurality of competing economic valuation models to evaluate
information relating to a plurality of available advertisement
placements. The real-time bidding machine facility 142 may deploy the
competing economic valuation models to predict an economic valuation for
each of the plurality of advertisement placements. After deploying the
plurality of economic valuation models, the real-time bidding machine
facility 142 may evaluate each valuation produced by each of the
plurality of competing economic valuation models to select one valuation
as a future valuation of an advertising placement. It will be understood
that general analytic methods, statistical techniques, and tools for
evaluating competing algorithms and models, such as valuation models, as
well as analytic methods, statistical techniques, and tools known to a
person of ordinary skill in the art are intended to be encompassed by the
present invention and may be used to evaluate competing algorithms and
valuation models in accordance with the methods and systems of the
present invention.

[0209] In another embodiment of the invention, after the real-time bidding
machine facility 142 receives a request to place an advertisement from a
publisher facility 112 the real-time bidding machine facility 142 may
deploy a plurality of competing economic valuation models to evaluate
information relating to a plurality of available advertisement
placements. The real-time bidding machine facility 142 may deploy the
competing economic valuation models to predict an economic valuation for
each of the plurality of advertisement placements. After deploying the
plurality of economic valuation models, the real-time bidding machine
facility 142 may evaluate in real time, each valuation produced by each
of the plurality of competing economic valuation models to select one
valuation as a future valuation of an advertising placement. It will be
understood that general analytic methods, statistical techniques, and
tools for evaluating competing algorithms and models, such as valuation
models, as well as analytic methods, statistical techniques, and tools
known to a person of ordinary skill in the art are intended to be
encompassed by the present invention and may be used to evaluate
competing algorithms and valuation models in accordance with the methods
and systems of the present invention. In an embodiment of the invention,
the future valuation may be based at least in part on simulation data
describing a future event. In an embodiment of the invention, the future
event may be a stock market fluctuation. Further, in an embodiment of the
invention, the simulation data describing future event may be derived
from analysis of historical event data.

[0210] In an embodiment of the invention, after the real-time bidding
machine facility 142 receives a request to place an advertisement from a
publisher facility 112, the real-time bidding machine facility 142 may
deploy a plurality of competing real-time bidding algorithms relating to
a plurality of available advertisement placements to bid for
advertisement placements. After deploying the plurality of competing
real-time bidding algorithms, the real-time bidding machine facility 142
may evaluate each bidding algorithm to select a preferred algorithm. In
an embodiment of the invention, the competing real-time bidding
algorithms may use data from a real-time bidding log. It will be
understood that general analytic methods, statistical techniques, and
tools for evaluating competing algorithms and models, such as valuation
models, as well as analytic methods, statistical techniques, and tools
known to a person of ordinary skill in the art are intended to be
encompassed by the present invention and may be used to evaluate
competing algorithms and valuation models in accordance with the methods
and systems of the present invention.

[0211] In another embodiment of the invention, after the real-time bidding
machine facility 142 receives a request to place an advertisement from a
publisher facility 112, the real-time bidding machine facility 142 may
deploy a plurality of competing real-time bidding algorithms relating to
a plurality of available advertisement placements. The real-time bidding
machine facility 142 may deploy the plurality of competing real-time
bidding algorithms to bid for advertisement placements. After deploying
the plurality of competing real-time bidding algorithms, the real-time
bidding machine facility 142 may evaluate each bid recommendation created
by the competing real-time bidding algorithms. The real-time bidding
machine facility 142 may reevaluate each bid recommendation created by
the competing real-time bidding algorithms to select one as a revised bid
recommendation. In an embodiment of the invention, the revised bid
recommendation may be based at least in part on a real-time bidding
algorithm using real-time event data 160 that was not available at the
time of selecting the bid recommendation. Thereafter, the real-time
bidding machine facility 142 may replace the bid recommendation with the
revised bid recommendation for use in deriving a recommended bid amount
for the advertising placement. In an embodiment of the invention, the
replacement may occur in real-time relative to the receipt of the request
to place an advertisement.

[0212] Referring now to FIG. 24 which illustrates a method 2400 for
selecting one among multiple competing valuation models in real-time
bidding for advertising placements, in accordance with an embodiment of
the invention. The method initiates at step 2402. At step 2404, in
response to receiving a request to place an advertisement, a plurality of
competing economic valuation models may be deployed to predict an
economic valuation for each of the plurality of advertisement placements.
Thereafter at step 2408, each valuation produced by each of the plurality
of competing economic valuation models may be evaluated to select one of
the valuation models as a current valuation of an advertising placement.
In embodiments of the invention, the economic valuation model may be
based at least in part on real-time event data, historic event data, user
data, contextual data, advertiser data, ad agency data, historical
advertising performance data, machine learning and third-party commercial
data. In an embodiment of the invention, the third party commercial data
may include financial data relating to historical advertisement
impressions. The method terminates at step 2410. It will be understood
that general analytic methods, statistical techniques, and tools for
evaluating competing algorithms and models, such as valuation models, as
well as analytic methods, statistical techniques, and tools known to a
person of ordinary skill in the art are intended to be encompassed by the
present invention and may be used to evaluate competing algorithms and
valuation models in accordance with the methods and systems of the
present invention.

[0213]FIG. 25 illustrates a method 2500 for replacing a first economic
valuation model by a second economic valuation model for deriving a
recommended bid amount for an advertising placement. The method initiates
at step 2502. At step 2504, in response to receiving a request to place
an advertisement, a plurality of competing economic valuation models may
be deployed to predict an economic valuation for each of the plurality of
advertisement placements. Thereafter at step 2508, valuations produced by
each of the plurality of competing economic valuation models may be
evaluated and a first valuation of an advertising placement may be then
selected. Further at step 2510, each valuation produced by each of the
plurality of competing economic valuation models may be reevaluated. One
of the competing economic valuation models may then be selected as a
revised valuation of an advertising placement. The revised valuation may
be based at least in part on analysis of an economic valuation model
using real-time event data that was not available at the time of
selecting the first valuation. Further at step 2512, the first valuation
may be replaced with the second revised valuation for use in deriving a
recommended bid amount for the advertising placement. In an embodiment of
the invention, the request may be received from a publisher and the
recommended bid amount may be automatically sent to the publisher. In
another embodiment of the invention, the request may be received from a
publisher and a bid equaling the recommended bid amount may be
automatically placed on behalf of the publisher. In yet another
embodiment of the invention, recommended bid amount may be associated
with a recommended time of ad placement. Still in another embodiment of
the invention, recommended bid amount may be further derived by analysis
of a real-time bidding log that is associated with a real-time bidding
machine. The method terminates at step 2514. It will be understood that
general analytic methods, statistical techniques, and tools for
evaluating competing algorithms and models, such as valuation models, as
well as analytic methods, statistical techniques, and tools known to a
person of ordinary skill in the art are intended to be encompassed by the
present invention and may be used to evaluate competing algorithms and
valuation models in accordance with the methods and systems of the
present invention.

[0214]FIG. 26 illustrates a method 2600 for evaluating multiple economic
valuation models and selecting one valuation as a future valuation of an
advertising placement, in accordance with an embodiment of the invention.
The method initiates at step 2602. At step 2604, in response to receiving
a request to place an advertisement, a plurality of competing economic
valuation models may be deployed. Information relating to a plurality of
available advertisement placements may be evaluated to predict an
economic valuation for each of the plurality of advertisement placements.
Further at step 2608, each valuation produced by each of the plurality of
competing economic valuation models may be evaluated to select one
valuation as a future valuation of an advertising placement. The method
terminates at step 2610. It will be understood that general analytic
methods, statistical techniques, and tools for evaluating competing
algorithms and models, such as valuation models, as well as analytic
methods, statistical techniques, and tools known to a person of ordinary
skill in the art are intended to be encompassed by the present invention
and may be used to evaluate competing algorithms and valuation models in
accordance with the methods and systems of the present invention.

[0215] FIG. 27 illustrates a method 2700 for evaluating in real time
multiple economic valuation models and selecting one valuation as a
future valuation of an advertising placement, in accordance with an
embodiment of the invention. The method initiates at step 2702. At step
2704, in response to receiving a request to place an advertisement, a
plurality of competing economic valuation models may be deployed.
Information relating to a plurality of available advertisement placements
may be evaluated to predict an economic valuation for each of the
plurality of advertisement placements. Thereafter at step 2708, each
valuation produced by each of the plurality of competing economic
valuation models may be evaluated in real-time to select one valuation as
a future valuation of an advertising placement. In an embodiment of the
invention, the future valuation may be based at least in part on
simulation data describing a future event. In another embodiment of the
invention, the future event may be a stock market fluctuation. In an
embodiment of the invention, the simulation data describing future event
may be derived from analysis of historical event data that may be chosen
based at least in part on contextual data relating to an advertisement to
be placed in the advertising placement. The method terminates at step
2710. It will be understood that general analytic methods, statistical
techniques, and tools for evaluating competing algorithms and models,
such as valuation models, as well as analytic methods, statistical
techniques, and tools known to a person of ordinary skill in the art are
intended to be encompassed by the present invention and may be used to
evaluate competing algorithms and valuation models in accordance with the
methods and systems of the present invention.

[0216]FIG. 28 illustrates a method 2800 for evaluating multiple bidding
algorithms to select a preferred algorithm for placing an advertisement,
in accordance with an embodiment of the invention. The method initiates
at step 2802. At step 2804, in response to receiving a request to place
an advertisement, a plurality of competing real-time bidding algorithms
may be deployed. The bidding algorithms may be related to a plurality of
available advertisement placements to bid for advertisement placements.
Thereafter at step 2808, each bidding algorithm may be evaluated to
select a preferred algorithm. The method terminates at step 2810. It will
be understood that general analytic methods, statistical techniques, and
tools for evaluating competing algorithms and models, such as valuation
models, as well as analytic methods, statistical techniques, and tools
known to a person of ordinary skill in the art are intended to be
encompassed by the present invention and may be used to evaluate
competing algorithms and valuation models in accordance with the methods
and systems of the present invention.

[0217]FIG. 29 illustrates a method 2900 for replacing a bid
recommendation with a revised bid recommendation for an advertising
placement, in accordance with an embodiment of the invention. The method
initiates at step 2902. At step 2904, in response to receiving a request
to place an advertisement, a plurality of competing real-time bidding
algorithms relating to a plurality of available advertisement placements
to bid for advertisement placements may be deployed. At step 2908, each
bid recommendation created by the competing real-time bidding algorithms
may be evaluated. Further at step 2910, each bid recommendation created
by the competing real-time bidding algorithms may be reevaluated to
select one as a revised bid recommendation. In an embodiment, the revised
bid recommendation is based at least in part on a real-time bidding
algorithm using real-time event data that was not available at the time
of selecting the bid recommendation. Thereafter at step 2912, the bid
recommendation may be replaced with the revised bid recommendation for
use in deriving a recommended bid amount for the advertising placement.
In an embodiment of the invention, the replacement may occur in real-time
relative to the receipt of the request to place an advertisement. The
method terminates at step 2914. It will be understood that general
analytic methods, statistical techniques, and tools for evaluating
competing algorithms and models, such as valuation models, as well as
analytic methods, statistical techniques, and tools known to a person of
ordinary skill in the art are intended to be encompassed by the present
invention and may be used to evaluate competing algorithms and valuation
models in accordance with the methods and systems of the present
invention.

[0218]FIG. 30 illustrates a real-time facility 3000 for measuring the
value of additional third party data 164, in accordance with an
embodiment of the invention. The real-time facility 2700 may include a
learning machine facility 138, a valuation algorithm facility 140, a
real-time bidding machine facility 142, additional third party dataset
3002, a bid request message 3004 from a publisher facility 112, and a
tracking facility 144. In an embodiment of the invention, the real-time
bidding machine facility 142 may receive a bid request message 3004 from
the publisher facility 112. The real-time bidding machine facility 142
may be considered a "real-time" facility since it may reply to a bid
request that is associated with time constraint. The real-time bidding
machine facility 142 may perform the real-time calculation using
targeting algorithms provided by the learning machine facility 138. In an
embodiment of the invention, the real-time bidding machine facility 142
may deploy an economic valuation model to perform the real-time
calculation.

[0219] In embodiments, the learning machine facility 138 may obtain a
third party data set 3002 to refine an economic valuation model. In an
embodiment of the invention, the third party dataset 2702 may include
data relating to users of advertising content. In embodiment of the
invention, the data relating to users of advertising content may include
demographic data, transaction data, conversion data, or some other type
of data. In another embodiment of the invention, the third party dataset
may include contextual data 162 relating to the plurality of available
placements, and/or plurality of advertisements. In embodiments of the
invention, the contextual data 162 may be derived from a contextualizer
service 132 that may be associated with the learning machine facility
138. In yet another embodiment of the invention, the third party dataset
3010 may include financial data relating to historical advertisement
impressions. Further, in embodiments of the invention, the economic
valuation model may based at least in part on real-time event data,
historic event data 154, user data 158, third-party commercial data,
advertiser data 152, and advertising agency data 152.

[0220] In an embodiment of the invention, the real-time bidding machine
facility 142 may receive an advertising campaign dataset and may split
the advertising campaign dataset into a first advertising campaign
dataset and a second advertising campaign dataset. Thereafter, the
real-time bidding machine facility 142 may deploy an economic valuation
model that may be refined through machine learning to evaluate
information relating to a plurality of available placements, and/or
plurality of advertisements, to predict an economic valuation for
placement of ad content from the first advertising campaign dataset. In
an embodiment of the invention, the machine learning may be based at
least in part on a third party dataset. The machine learning may be
achieved by the learning machine facility 138. After the refinement of
the evaluation model, the real-time bidding machine facility 142 may
place ad content from the first and second advertising campaign datasets
within the plurality of available placements, and/or plurality of
advertisements. Content from the first advertising campaign may be placed
based at least in part on the predicted economic valuation, and content
from the second advertising campaign dataset may be placed based on a
method that does not rely on the third party dataset. The real-time
bidding machine facility 142 may further receive impression data from a
tracking machine facility 144 that may relate to the ad content placed
from the first and second advertising campaign datasets. In an embodiment
of the invention, the impression data may include data regarding user
interactions with the ad content. Thereafter, the real-time bidding
machine facility 142, may determine a value of the third party dataset
based at least in part on a comparison of impression data relating to the
ad content placed from the first and second advertising campaign
datasets.

[0221] Further, in an embodiment of the invention, the real-time bidding
machine facility 142 may compute a valuation of the third party dataset
3002 based at least in part on a comparison of advertising impression
data relating to ad content placed from first and second advertising
campaign datasets. In an embodiment of the invention, the placement of
the ad content from the first advertising campaign dataset may be based
at least in part on a machine learning algorithm employing the third
party dataset 2710 to select optimum ad placements. Thereafter, the
real-time bidding machine facility 142 may bill an advertiser 104 a
portion of the valuation to place an ad content from the first
advertising campaign dataset. In an embodiment of the invention, the
computation of the valuation and the billing of the advertiser 104 may be
automatically performed upon receipt of a request to place content from
the advertiser 104. In another embodiment of the invention, the
computation of the valuation may be the result of the comparison of the
performance of multiple competing valuation algorithms 140. In an
embodiment of the invention, the comparison of the performance of
multiple competing valuation algorithms 140 may include the use of
valuation algorithms 140 based at least in part on historical data. It
will be understood that general analytic methods, statistical techniques,
and tools for evaluating competing algorithms and models, such as
valuation models, as well as analytic methods, statistical techniques,
and tools known to a person of ordinary skill in the art are intended to
be encompassed by the present invention and may be used to evaluate
competing algorithms and valuation models in accordance with the methods
and systems of the present invention.

[0222] Further in an embodiment of the invention, the real-time bidding
machine facility 142 may compute a valuation of a third party dataset
3010 based at least in part on a comparison of advertising impression
data relating to ad content placed from first and second advertising
campaign datasets. In an embodiment of the invention, the placement of
the ad content from the first advertising campaign dataset may be based
at least in part on a machine learning algorithm employing the third
party dataset 3010 to select optimum ad placements. Thereafter, the
real-time bidding machine facility 142 may calibrate a bid amount
recommendation for a publisher 112 to pay for a placement of an ad
content based at least in part on the valuation. In an embodiment of the
invention, the calibration may be adjusted iteratively to account for
real-time event data 160 and its effect on the valuation.

[0223]FIG. 31 illustrates a method 3100 for advertising valuation that
has the ability to measure the value of additional third party data in
accordance with an embodiment of the invention. The method initiates at
step 3102. At step 3104, an advertising campaign dataset may be split
into a first advertising campaign dataset and a second advertising
campaign dataset. At step 3108, an economic valuation model that may be
refined through machine learning, may be deployed to evaluate information
relating to a plurality of available placements, and/or plurality of
advertisements to predict an economic valuation for placement of ad
content from the first advertising campaign dataset. In an embodiment of
the invention, the machine learning may be based at least in part on a
third party dataset. At step 3110, ad content from the first and second
advertising campaign datasets may be placed within the plurality of
available placements, and/or plurality of advertisements. In an
embodiment of the invention, content from the first advertising campaign
may be placed based at least in part on the predicted economic valuation,
and content from the second advertising campaign dataset may be placed
based on a method that does not rely on the third party dataset. Further
at step 3112, impression data from a tracking machine facility relating
to the ad content placed from the first and second advertising campaign
datasets may be received. In an embodiment, the impression data may
include data regarding user interactions with the ad content. Thereafter,
at step 3114, a value of the third party dataset based at least in part
on a comparison of impression data relating to the ad content placed from
the first and second advertising campaign datasets may be determined. In
an embodiment of the invention, the third party dataset may include data
relating to users of advertising content, contextual data relating to the
plurality of available placements, and/or plurality of advertisements, or
financial data relating to historical advertisement impressions. In an
embodiment of the invention, data relating to users of advertising
content may include demographic data, transaction data or advertisement
conversion data. In an embodiment of the invention, contextual data may
be derived from a contextualizer service that is associated with the
machine learning facility. In an embodiment of the invention, economic
valuation model may be based at least in part on real-time event data,
part on historic event data, part on user data, part on third-party
commercial data, part on advertiser data or part on advertising agency
data. The method terminates at step 3118.

[0224] FIG. 32 illustrates a method 3200 for computing a valuation of a
third party dataset and billing an advertiser a portion of the valuation,
in accordance with an embodiment of the invention. The method initiates
at step 3202. At step 3204, a valuation of a third party dataset may be
computed based at least in part on a comparison of advertising impression
data relating to ad content placed from first and second advertising
campaign datasets. In an embodiment of the invention, the placement of
the ad content from the first advertising campaign dataset may be based
at least in part on a machine learning algorithm employing the third
party dataset to select optimum ad placements. Thereafter, at step 3208,
an advertiser may be billed a portion of the valuation to place an ad
content from the first advertising campaign dataset. In an embodiment of
the invention, the computation of the valuation and the billing of the
advertiser may be automatically performed upon receipt of a request to
place content from the advertiser. In another embodiment of the
invention, computation of the valuation may be the result of comparing
the performance of multiple competing valuation algorithms. In an
embodiment of the invention, comparison of the performance of multiple
competing valuation algorithms may include the use of valuation
algorithms based at least in part on historical data. The method
terminates at step 3210. It will be understood that general analytic
methods, statistical techniques, and tools for evaluating competing
algorithms and models, such as valuation models, as well as analytic
methods, statistical techniques, and tools known to a person of ordinary
skill in the art are intended to be encompassed by the present invention
and may be used to evaluate competing algorithms and valuation models in
accordance with the methods and systems of the present invention.

[0225]FIG. 33 illustrates a method 3300 for computing a valuation of a
third party dataset and calibrating a bid amount recommendation for a
publisher to pay for a placement of an ad content based at least in part
on the valuation, in accordance with an embodiment of the invention. The
method initiates at step 3302. At step 3304, a valuation of a third party
dataset may be computed based at least in part on a comparison of
advertising impression data relating to ad content placed from first and
second advertising campaign datasets. In an embodiment of the invention,
the placement of the ad content from the first advertising campaign
dataset may be based at least in part on a machine learning algorithm
employing the third party dataset to select optimum ad placements.
Thereafter, at step 3308, a bid amount recommendation for a publisher to
pay may be calibrated for a placement of an ad content based at least in
part on the valuation. In an embodiment of the invention, calibration may
be adjusted iteratively to account for real-time event data and its
effect on the valuation. The method terminates at step 3310.

[0226] In embodiments, the analytic output of the analytic platform 114
may be illustrated using data visualization techniques including, but not
limited to the surface charts shown in FIGS. 34-38. Surface charts may
illustrate places of efficiency within, for example, the performance of
an advertising campaign, where the height of the surface measures a
conversion value per ad impression which is indexed to average
performance. In an embodiment, surface areas with a value greater than
one (1) may indicate better average conversion value and areas below one
(1) may indicate underperformance. A confidence test may be applied to
account for lower volume cross-sections of a surface chart and its
associated data. FIG. 34 depicts a data visualization embodiment
presenting a summary of advertising performance by time of day versus day
of the week. FIG. 35 depicts a data visualization embodiment presenting a
summary of advertising performance by population density. FIG. 36 depicts
a data visualization embodiment presenting a summary of advertising
performance by geographic region in the United States. FIG. 37 depicts a
data visualization embodiment presenting a summary of advertising
performance by personal income. FIG. 38 depicts a data visualization
embodiment presenting a summary of advertising performance by gender.

[0227] FIG. 39 illustrates an affinity index, by category, for an
advertising campaign/brand. The methods and system of the present
invention may identify characteristics of consumers that are more likely
than the general population to be interested in an advertiser brand. The
methods and systems may also identify characteristics of consumers that
are less likely than the general population to be interested in the
advertiser brand. On the left side of the chart in FIG. 39, the
characteristics of consumers that are more interested are presented. The
chart also shows an index that represents how much more likely than the
general population those consumers are to be engaged with the advertiser
brand. The right side of the chart presents the characteristics of
consumers that are less interested, and shows an index that represents
how much less likely than the general population those consumers are to
be engaged with the brand. Indexes, such as that presented in FIG. 39 may
take into account the size of the sample, and use a formulation that
incorporates sample size and uncertainty ranges.

[0228]FIG. 40 depicts a data visualization embodiment presenting a
summary of page visits by the number of impressions. The methods and
system of the present invention may identify the conversion rates that
different cohorts of consumers present. As shown in FIG. 40, each cohort
may be defined by the number of ads shown to consumer-members of the
cohort. The analytic platform 114 may analyze the consumers who saw a
given number of ads and compute a conversion rate. The analytic platform
114 may take into account only impressions that were shown to consumers
prior to the consumer executing the action, based at least in part on
data included in an impression log 148. As an example, a consumer who has
seen 3 ads before performing an action desirable to the advertiser is
member of cohort 3. The other 10 members of cohort 3 might have seen 3
ads, but might have not perform any action deemed beneficial to the
advertiser. The conversion rate for cohort 3 is 3/10=0.3 or 300,000 per
million consumers. The analysis takes into account the size of the
sample, and uses a formulation that incorporates sample size and
uncertainty ranges. The analysis also fits a curve that most likely
represents the behavior observed across all cohorts.

[0229] The ability to measure advertising campaign results is a priority
of a majority of advertising systems. Measured advertising campaign
results, including results that are categorized by user, user groups, and
the like, may be subsequently utilized by advertisers to modify
advertising campaigns to maximize the effect of the advertisement
messages on intended user and/or user group targets. For example, an
advertiser may modify its campaigns by reallocating budgets and prices,
from lower performing ones to focus on user groups that have a history of
responsiveness to the campaign, similar campaigns, or advertisements that
share an attribute(s) with material contained within an advertising
campaign. Additionally, a plurality of media channels may be used for
communicating the advertising campaign to consumers.

[0230] For online advertising, it may be possible to measure the effect of
advertisements by using consumer identifiers stored in cookies. This
enables an advertiser to distinguish individuals, while keeping their
identity anonymous. However, there are cases where it is not possible or
desirable to distinguish individuals. In embodiments of the present
invention, methods and systems are provided for an advertising
measurement solution for cases where it may not be possible or desirable
to identify individuals. For example, using the methods and systems of
the present invention it may be possible to measure multiple
characteristics that may describe a media channel to link advertising
messages shown and their subsequent effect on consumers and consumer
groupings. This may permit measure of campaign effectiveness, advertising
success, and the like, even when the measurement of effect may not be
feasible using conventional methods, as it may not be possible or
desirable to identify individuals. Examples of such use cases include,
but are not limited to, the measurement of advertising across different
channels (e.g., TV and online media) and measurement of online
advertising without the use of cookie identifiers.

[0231] In accordance with various embodiments of the present invention,
several characteristics of media may be utilized to enable the creation
of small segments that may contain anywhere from one or a plurality of
individuals, all of whom may share one or more characteristics.
Characteristics may include, but are not limited to, a time of day (e.g.,
the time of day that an advertisement is viewed), a geographic region, an
individuals' interest in a type of content. Each characteristic, or
combination of characteristics may be used to define and/or describe a
set of individuals. Therefore, the characteristics (such as time of the
day, day of the week, browser and operating system used, screen
resolution, geographic region, and type of content/content category) may
be used as targeting parameters.

[0232] Targeting parameters may vary among media channels in terms of
nature of these channels. For example, channel A might have only three
parameters available, while channel B may have more than 40. Moreover,
the nature of these parameters may change. For example, for print media,
an advertiser may consider the parameters as edition of a magazine, type
or genre of the magazine, and the size of the advertisement on a physical
page, such as a magazine page, or some other parameter. Similarly, for TV
advertising, the parameters may be the time the advertisement was shown,
its duration, and whether it included a product shot at the end, or some
other parameter.

[0233] In embodiments, it may be possible to use a combination of multiple
parameters (available to a channel) to name definite sections of the
channel, irrespective of the channel being chosen by the advertiser.
Also, channel sections may be small in some cases and describe few
individuals, but may be defined nonetheless by using as many targeting
parameters as possible. Different channels may be linked based on an
assumption that individuals reached by those channels behave in the same
way. For example, a sports enthusiast may be assumed to watch sports on
TV, and to also follow sports on the web and print media.

[0234] In embodiments of the present invention, a set of targeting
parameters, defining a set of users reached through a specific channel,
may be used to create a Synthetic User Identifier (SUID). The SUID may be
stored on a server side system such that it, or an accumulation of them
may be used to project advertisement channel segmentation in the future.
For example, an ad placement or ad interaction may cause the collection
and extraction of user, device, and/or contextual information from the
placement, interaction or client device. A SUID may describe several
individuals, but in specific cases (by adding multiple parameters) it may
describe a unique individual. For example, a special combination of
software loaded, the Internet Protocol (IP) address, the type of
operating system and screen resolution, and content interest may describe
a specific individual or a set of individuals. In another embodiment,
users may be tagged by several SUIDs. For example, a user may follow
sports content from 3 pm to 6 pm, and follow news content from 7 pm to 10
pm in the same geographic region. Each of the combinations (i.e., 3-6 pm,
sports, and 7-10 pm, news) may have its own SUID. Additionally, in an
embodiment of the present invention, the effect of the advertisements in
a small crowd of users may be measured. For this purpose, success may be
measured each time it is observed. Success may be defined as a particular
action at the advertiser's website, such as an ad conversion,
click-through, or some other behavior. When a user executes particular
actions on the advertiser's website, for example, the actions may also
reveal information relating to when the advertisement was received.
Parameters such as content category (e.g., of the referral URL),
geographical location, time of the day, day of the week, browser used,
operating system, screen resolution, or some other data may be recorded
by the advertiser's website and/or an agent working in coordination with
such website. As a consequence, using the methods and systems as
described herein, it may be possible to establish a statistical link
between online advertisements shown and actions achieved at the
advertiser's website. Furthermore, when using media and advertisements
shown off-line, it may be possible to rely on coarser metrics and
distribute the positive outcome measured by the advertiser across a wider
population (described by multiple SUIDs). In an example, it may not be
possible to link a T.V. advertisement with a specific user's screen
resolution and operating system. Nevertheless, the geographical
information, the type of content, and the time and date of the T.V.
advertisement may be indicators of the types of users targeted through
such advertisement. Furthermore, for T.V. advertisements, the count of
users receiving an advertisement, and other data may be acquired through
off-line surveys. This data may be used to measure the number of members
for each SUID.

[0235] In some sample scenarios, it may not be possible to link the sales
result at a specific advertiser's store to either specific consumers or
advertisements. However, it may be possible to link the sales result to a
limited number of zip codes as revealed by the addresses of consumers
buying at the store. Furthermore, it may be possible to overlay the
timeline of the advertisements shown versus the timeline of the sales
results. In accordance with an embodiment of the present invention, the
sales result for a given week may be allocated to SUIDs that capture
information regarding zip codes in proximity to the store. The proportion
of sales allocated to each zip code may be driven by the data captured by
the point-of-sale (POS) system, which may, for example, provide a
proportion based on count of individuals, the sum of revenue driven by
each zip code, or some other analytic measure. In another embodiment, a
telephone order may be traced to a geographic area, representative of the
area code of the caller. If additional information is captured, the
result may be linked to the zip code address of the buyer, including the
"zip+4" address, which may enable mapping.

[0236] The ability to identify unique users (or small groups of users),
deliver advertising to them, and link the performance of such
advertisements to those users may further enable a granular measurement
of advertisement and advertisement campaign success and facilitate
adjustment of price or amount to pay to access and invest in such media
further using the methods and systems as described herein. Cross-channel
attribution may be enhanced and stimulated by the use of couponing that
may enable validation of inferred links between different SUIDs.

[0237] Referring to FIG. 57, in embodiments, the presently disclosed
invention may provide methods and systems 5700 for creating, at a server
facility, a plurality of Synthetic User Identifiers by associating an
advertisement with the advertisement's impression data and at least two
of user, device, and contextual information as derived from a plurality
of users' interactions with the advertisement 5704. One or more databases
may include a contextual database that may provide contextual data,
associated with advertisers, advertiser's content publishers, publisher's
content (e.g., a publisher's website), and the like. The contextual
database(s) may be provided within the analytic platform 114 or
associated with the analytic platform, as described herein. Contextual
data, may include, but is not limited to, keywords found within the ad;
an URL associated with prior placements of the ad, or some other type of
contextual data, and may be stored as a categorization metadata relating
to publisher's content, as described herein. In an example, such
categorization metadata may record that a first publisher's website is
related to music content, and a second publisher's content is
predominantly automobile-related. The Synthetic User Identifiers may be
stored in a database that is accessible to the server facility and
separate from a client system 5708. The server facility may be may be
provided within the analytic platform 114 or associated with the analytic
platform, as described herein. The plurality of Synthetic User
Identifiers may be analyzed for correlations that indicate an
advertisement type may produce a predetermined conversion rate if
presented to an advertisement channel 5710, and a targeted advertisement
may be recommended, which is associated with the advertisement type, to
be presented to the advertisement channel 5712. The analysis, may include
the usage of machine learning and matrix-based techniques, as described
herein. Examples of machine learning algorithms may include, but are not
limited to, Naive Bayes, Bayes Net, Support Vector Machines, Logistic
Regression, Neural Networks, and Decision Trees. These algorithms may be
used to produce classifiers, which are algorithms that classify whether
or not an advertisement is likely to produce an action or not. In their
basic form, they return a "yes" or "no" answer and a score indicated the
strength of certainty of the classifier. More complicated predictors may
be used. When calibration techniques are applied, they return a
probability estimate of the likelihood of a prediction to be correct.
Calibration techniques can also indicate which specific advertisement is
most likely to produce a desired user action or which characteristics
describe advertisings most likely to produce an action.

[0238] In embodiments, the step of recommending a targeted advertisement
may involve recommending a bid amount for the targeted advertisement,
recommending a budget allocation for the targeted advertisement, or some
other type of recommendation. Recommending may involve partitioning an
advertisement inventory based on the Synthetic User Identifier.

[0239] In embodiments, the plurality of users' interactions with the
advertisement may derive from a plurality of advertising channels. The
plurality of advertising channels may include online and offline
advertising channels. Online advertising channels may include a website.
Offline advertising channels may include a print medium.

[0240] In embodiments, contextual information may be a device
characteristic, an operating system, an advertising medium type, a
plurality of contextual information, a user demographic, or some other
type of contextual information.

[0241] Referring to FIG. 58, in embodiments, the presently disclosed
invention may provide methods and systems 5800 for categorizing a
plurality of available advertising channels, wherein each of the
available advertising channels is categorized based at least in part on
contextual information 5804, impression history, advertising channel
performance characteristics, or some other type of data. For example, the
tracking machine facility 144, as described herein, may record the ID of
an ad requestor, user, or other information that labels the user
including, but not limited to, Internet Protocol (IP) address, context of
an ad and/or ad placement, a user's history, geo-location information of
the user, social behavior, inferred demographics, advertising
impressions, user clickthroughs, action logs, or some other type of data,
and use this data to categorize available advertising channels. An
advertising impression log relating to prior advertising placements
within the plurality of categorized available advertising channels may be
analyzed, using the statistical techniques as described herein, wherein
the analysis produces a quantitative association between a user and at
least one of the available advertising channels, the quantitative
association expressing at least in part a probability of the user
recording an advertising conversion within at least one of the available
advertising channels 5808. The quantitative association may be stored as
a Synthetic User Identifier 5810, and an advertisement may be selected to
present to the user within at least one of the available advertising
channels based at least in part on the Synthetic User Identifier 5812.
Further, the real-time bidding machine facility 142 may use economic
valuation model to further classify each of a plurality of available
advertisements. The classification may be a datum indicating a
probability of each of the available advertising placements achieving an
advertising impression. The real-time bidding machine facility 142 may
then prioritize the available advertising placements based at least in
part on the datum indicating the probability of achieving an advertising
impression in addition to using the Synthetic User Identifier.
Thereafter, the real-time bidding machine facility 142 may select and
present to a user at least one of the plurality of available placements,
and/or plurality of advertisements, based on the prioritization.
Available advertising channels may also be prioritized using similar
statistical methods based at least in part on the Synthetic User
Identifier and bidding data or some other type of data used by the
analytic platform 114, as described herein.

[0242] In embodiments, the selected advertisement may be presented to a
second user that shares an attribute of the user with whom the user
Synthetic User Identifier is associated.

[0243] In embodiments, a failure of the user to register a new impression
following presentation of the selected advertisement is used by a
learning machine facility to update the quantitative association.

[0244] In embodiments, a plurality of Synthetic User Identifiers, each
bearing a quantitative association with the other, may be tagged as a
consumer cohort to which advertisers may bid on the opportunity to
present advertisements using a real-time bidding machine facility. The
analysis may include using an economic valuation model that is further
based in part on real-time bidding log data. The analysis may include
using an economic valuation model that is further based in part on
historical bidding data.

[0245] Referring to FIG. 59, in embodiments, the presently disclosed
invention may provide methods and systems 5900 for targeting the
placement of advertising within an available channel based at least in
part on contextual information, the system comprising: a computer having
a processor and software which is operable on the processor. The software
may include an analytics platform facility that includes at least a
learning machine and a valuation algorithms facility. The software may be
adapted to: (i) create, at a server facility, a plurality of Synthetic
User Identifiers by associating an advertisement with the advertisement's
impression data and at least two of user, device, and contextual
information as derived from a plurality of users' interactions with the
advertisement 5904; (ii) store the Synthetic User Identifiers in a
database accessible to the server facility and separate from a client
system 5908; (iii) use the Synthetic User Identifiers to target
advertisements to consumers, wherein at least one of the amount, timing
or duration of advertising presented to consumers is varied across
available advertising channels based at least in part by use of the
Synthetic User Identifiers 5910; (iv) analyze the plurality of Synthetic
User Identifiers for correlations that indicate an advertisement type may
produce a predetermined conversion rate if advertisements are presented
through an advertisement channel and with an intensity level, wherein the
intensity level is at least one of the amount, timing or duration of the
advertising presented 5912; and (v) recommend, for each specific
Synthetic User Identifier, an adjusted intensity of advertising
associated with the advertisement type, to be presented through each
advertisement channel 5914.

[0246] In an embodiment, the assignment of effect achieved by mapping
advertising results (identified by different SUIDs) to the SUIDs of the
advertisements may be governed by a matrix (M). This matrix may represent
a probabilistic model that may disclose overlap between different SUIDs.
The matrix (M) may have a column for each possible `Effect Synthetic User
ID` (EID) and rows for each Channel Synthetic User ID (CID). The sum of
coefficients in each given row of matrix M will add to 1.

[0247] The coefficients for each specific cell row i, column j of matrix M
may be computed by calculating the probability that a certain number of
CIDi will have an effect on EIDj These probabilities may then be
normalized to 1 for each given row i column j. The normalization may be
needed as CIDs may overlap (e.g., an individual who is a sports
aficionado online, might also be targeted through an outdoor panel in a
highway). A vector CID of attribution may be computed by multiplying the
vector that expresses the effects EID times the matrix (M) through the
matricial product.

[0248]FIG. 41 depicts an example of matrix operations (including M
effects matrix 4102, CID vector 4104, and EID vector 4108) that may be
used to map the number of impressions as expressed through the channel ID
to affect the store sales may be provided.

[0249] FIG. 42 illustrates an example of parameters that may create a SUID
partition of the advertisement inventory. The parameters include time of
the day in which advertisement is placed (4202), geographical region
where the consumer is located (4204), content category along which an
advertisement is placed (4208), size of the online advertisement (4210),
and browser used to load the advertisement (4212).

[0250]FIG. 43 illustrates an example of a feedback loop for offline data
and online data to advertising.

[0251] Referring to FIG. 44, a number of internal machines (including
hardware and software components) and services such as a real time
bidding machine facility 142, tracking machine facility 144, real time
bidding logs 150, impression, click, and action logs 148, and learning
machine facility 138 among others, as described herein, that may be used
for managing and tracking the advertisement activities in association
with SUIDs.

[0252] In embodiments, the real time bidding machine facility 142 may
receive bid request messages from an Advertising Distribution Service
(ADS) 122. It may be considered as a real time system since bid requests
may be responded within certain time constraints. The real time bidding
machine facility 142 may also calculate which advertising message to
show, while the user is waiting for the system to decide. Data such as
SUIDs may be used to model bidding and valuation based at least in part
on historical data associated with the SUIDs, such as advertisement
success, advertisement conversions, and the like. The system may perform
the real time calculations such as by dynamically estimating an optimal
bid value using algorithms that include SUIDs that are provided at least
in part by the learning machine facility 138.

[0253] The real time bidding logs 150 may include records of bid requests
received and bid responses sent by the real time bidding machine facility
142. These logs may contain data regarding the sites visited by the user.
This may be further used to derive user interests, browsing habits, and
to compute SUIDs. Additionally, these logs may record the rate of arrival
of advertising placement opportunities from different channels.

[0254] In embodiments, the learning machine facility 138 may be used to
develop targeting algorithms for the real time bidding engine, including
targeting algorithms that are based at least in part on SUIDs. It may
adopt patterns, including social behavior, inferred demographics,
inferred SUIDs, among others, which may be used to better target online
advertisements. The learning machine facility 138 may also utilize the
impression, click, and action logs 148 produced by the tracking system.

[0255] The interaction and coordination among the various machines may be
described using a scenario where an advertiser A places an "order" with
instructions limiting and/or describing location and time for an
advertisement placement. In an embodiment, these instructions may include
the selection of targeting parameter, such as SUIDs provided by the
methods and systems, as described herein. The order may then be executed
across multiple channels. The advertiser may specify a criterion of
`goodness` for the campaign to be successful. A `goodness` criteria may
be measured through specific metrics that may be tracked through
recording of activities that the user may complete at the advertiser
website, or through off-line purchases, visits or other interactions with
the advertiser.

[0256] Continuing the example, as a next step, the system may divide the
available channels to place advertisements (online and offline) into
smaller sections, for example where each section represents a SUID. The
division may be based on a combination of parameters such as time of day,
day of week, type of content, user geographical location, user browser,
or some other data type. In an example, the division for T.V. media can
be based on geography, time of day, day of week, type of content, and the
like. For magazines, the division may be based on month of the year,
geography (for magazines running multiple advertising regions), and type
of content. The criteria of `goodness` specified by the advertiser and
the distribution of positive outcomes may be codified so that a positive
outcome can be assigned to one or more SUIDs. For online advertisements,
the combination of parameters may result in highly granular links that
identify a few users for each SUID.

[0257] In embodiments, a learning system may be used to leverage the
information pertaining to which SUIDs were more successful in creating
desired outcomes versus others. This learning system may develop
customized targeting algorithms based on what has been successful. The
algorithms may calculate an expected value of the advertisement based on
the given conditions, and may seek to maximize the specified `goodness`
criteria.

[0258] In the case of real time bidding, algorithms may be received by the
real time bidding machine facility 142, which may wait for opportunities
to place the advertisement. Bid requests may be received by the real-time
bidding machine. Each request may be evaluated for its value for each
advertiser, using the received algorithms (which may utilize SUIDs). Bid
responses may be sent for advertisements that have an attractive value.
Lower values may be bid if they are estimated appropriately. The bid
response requests may then be placed at a particular price.

[0259] On the other hand, in the case of non-real time advertisement
purchases, algorithms may be received by a non-real time order creation
system that will decide how much budget to allocate to each advertising
channel, with the degree of granularity as the advertising channel
supports. For example, it may not be possible to buy T.V. spots at a
specific hour, but may be in another programming time slot, such as
morning, afternoon, evening, or night. For non-real time advertisement
purchases, metrics about advertisements running times, reach, and other
parameters may be collected through off-line methods, and the related
data may be added to the system.

[0260] For online media, the tracking machine facility 144 may log
advertisement impressions, user clicks, and/or user actions. The tracking
machine facility 144 logs may be further sent to the learning system,
which may use the `goodness criteria` and decide regarding the
improvement and customization of algorithms. This process may be an
iterative process.

[0261] In accordance with various embodiments, the present invention
facilitates grouping of users (as required) to describe them through
media, consumer, and creative attributes that the users share. Each of
these groups may be assigned an SUID, which describes groups as
granularly as possible. In the case of online, mobile, and video over IP
content, combined SUIDs may result in describing very few individuals or
just one. Simultaneous tagging of users with multiple SUIDs may be
possible. However, the degree of granularity for each SUID and parameters
that describe each SUID may vary across channels or for other reasons.
Nevertheless, identification of positive results, and linking of positive
results with one or more SUIDs, may be possible for the advertiser using
the methods and systems, as described herein. Further, the present
invention may facilitate the creation of a feedback data process whereby
data from advertisements placed under each SUID may be aligned with the
results achieved, even when it may not be possible to map each
advertisement and unique individual with a result. In embodiments, the
present invention may enable automatic reallocation of budgets across
channels.

[0262] In accordance with an embodiment of the present invention, methods
and systems for global yield management for buyers and sellers of digital
and analog media that may measure and maximize the performance of
advertising campaigns is provided. Examples of digital media may include,
but are not limited to, display advertisements, video advertisements,
mobile advertisements, search advertisements, email advertisements, IPTV,
and digital billboards. Examples of analog media may include, but are not
limited to, radio, outdoors panels, indoors panels, print media, or some
other type of analog media.

[0263] In embodiments, the methods and systems may enable a reverse
auction that may allow buyers to maximize their results. In an example,
sellers of advertisements may connect with the Global Yield Manager-Buyer
(GYM-B 4712) system, calling it when trying to sell one or a plurality of
advertisement opportunities. Buyers may observe the offer to sell and
make purchase decisions, seeking to maximize their own benefit. In any of
these cases, the system may keep record, and observe rules about which
advertisers are allowed for each publisher and vice versa.

[0264] In an embodiment, a buyer may call the seller asking for
advertisements to be sold. In another embodiment, the system may look to
the buyer as an ad server that may be called each time the seller decides
to offer an opportunity to place one or more advertisements to the buyer.
In a simplified example, there may be a single advertiser associated with
the Global Yield Management system. In such a case, there may not be
options available from the buyers' perspective (i.e., all impressions
provided by the publisher may be used). In addition, the price to pay for
each advertisement placement opportunity may be fixed and the advertiser
may have multiple versions of the advertisement that may be used for each
placement opportunity. In this case, the GYM-B 4712 may decide in only
one dimension: which creative(s) to show and the optimization may seek to
maximize the campaign performance, as measured by the success metric for
such a campaign. Further, GYM-B 4712 may have specific performance goals
for each publisher associated with the GYM-B 4712; and when those goals
are not achieved, it may trigger an automated email, communicating this
face to the operator and/or publisher.

[0265] In another example, there may be a single advertiser associated
with the Global Yield Management system and options may be available from
the buyers' perspective (i.e., the buyer may not use an impression and
may not pay for it). In addition, the price to pay for each advertisement
placement opportunity may be fixed and the advertiser may have multiple
versions of the advertisement that can be used for each placement
opportunity. In such a scenario, the GYM-B 4712 may decide on two
dimensions: whether to take an advertisement or a plurality of
advertisements, and which creative(s) to show. Further, the optimization
may seek to maximize the campaign performance, as measured by the success
metric for such campaign. The GYM-B 4712 may have specific performance
goals for each publisher associated with the GYM-B 4712, and when those
goals are not achieved, it may trigger an automated email, communicating
this to the operator and/or the publisher.

[0266] In an example embodiment to illustrate the concept of optionality,
an advertiser may include a publisher-advertiser deal involving a fixed
budget and price. In this case, the system may keep track of the
remaining publisher budget as time and purchases progress, and may
decrement the budget for each advertisement placed. The negotiated deal
may result in an "advertisement placement." Further, integration may be
achieved, at least in part, through standard advertisement tags.
Advertisement tags may be unique by publisher deal and pool (e.g.,
publishers may have multiple deals within a pool).

[0267] In an embodiments, inventory optionality may be provided. Thus, the
system may consume only an agreed to budget amount that is independent of
call volume. In an embodiment, the system may decide which calls to
accept. For unaccepted calls, the system may return a pre-assigned URL.
The pre-assigned URL may be decided by publisher, advertiser, and the
like. Advertisement tags may capture information such as URL of the page,
user agent information (OS, browser, resolution, etc.), cookie access
(for user ID, others if stored at cookie), IP address of user, ID of the
pool, ID of the publisher specific advertisement tag, and other
information that publishers may share (e.g. demographics from login). In
addition, advertisement tags may use Javascript or an alternative coding
for data capture. FIG. 45 illustrates a simplified embodiment of the
chain between publisher and advertisement networks, in accordance with an
embodiment of the present invention. In an embodiment, the system may
evenly distribute placement budget along all days where placement may be
active. Further, budget pacing may be independent of advertisement call
volume. Pacing may be held periodically (e.g., daily). In example
embodiments, monthly or lifetime pacing may be allowed. In other
embodiments, publishers may see an aggregated even pacing, even when
individual advertisers may buy more or less each day. Each publisher in
the GYM-B system may be a substitute for another, even if prices are
different.

[0268] In accordance with embodiments of the present invention, if a
campaign objective exists, then the system may maximize the value of the
placement. Mathematically, it may be represented as: Value of
placement=Sum of bids (as calculated by the Real Time System bidding
machine) minus sum of inventory cost (either the fixed or variable cost
agreed between the buyer and seller, and recorded in the pool database)).
Further, the system may maximize the sum of bids as inventory cost is
fixed. In case there is no campaign objective, the bid may be the CPM
price specified in the required fixed. A flight is understood as a
subdivision of a campaign, with an assigned budget, defined targeting
parameters that describe the media to use to show ads, and an specific
set of advertising messages and graphics to show using such media. An
advertising campaign is executed through one or more flights. Thus,
benefit may be achieved on consolidated buy and using all available data
for performance measurement and optimization. The pool may rely on RTS
4502 valuation to evaluate advertisement fitness.

[0269] In another embodiment, the data structures may be linked to GYM-B
4712 such that the GYM-B 4712 system holds multiple publisher placements.
The placements are to publishers, as behave like the campaign flights,
are to advertisers; the placement enables a publisher to exercise some
control as to how much budget to provide through each, and which
advertisers can use them. There may be a plurality of GYM-B 4712 system
attributes such as GYM-B 4712 system Name, Placements that belong to it,
Controlling entity (the controlling agency may be an advertiser, or an ad
agency or the like), Pool Budget, Flight it is linked to, Pool start and
end date (inventory must be bought), or some other attribute. In
embodiments, there may be a plurality of publisher placement attributes
such as Placement Name, Publisher name, Pool it belongs to, Placement
Budget, CPM price, call volume, Placement start and end date, Pass-back
advertisement tag, Placement-specific industries, advertisers' blacklist,
or some other attribute.

[0270] In accordance with various embodiments of the present invention,
user interface (UI) functionality may be provided for a GYM-B 4712
system. The UI may facilitate the functionality of the GYM-B 4712 system,
such as allocating budget to GYM-B 4712 system. The UI may facilitate the
selection of an inventory source type, and entering new GYM-B 4712 system
attributes, GYM-B 4712 system name, GYM-B 4712 system budget, advertiser
name, start and end dates inherited from flight, or some other attribute.
A newly created pool may appear only to the advertiser that created the
pool. Further, placements for each publisher in GYM-B 4712 system may be
created. Placements may be added using the UI in a manner similar to
adding flights to a campaign. For the creation of placements, variables
such as placement name, publisher name, placement budget, CPM price, call
volume, placement start and end date, pass-back advertisement tag,
Placement-specific industries, advertisers' blacklist, and the like may
be provided. The UI may provide advertisement tags to send to the
publisher. Subsequently, this may be integrated with, for example,
emails. The UI may also include additional screens to add placements
similar to adding flights.

[0271] The UI may also provide access to reporting such as pool level
reporting, placement level reporting, placement level performance, top
level domain reporting, billing reporting for reconciliation, and the
like.

[0272] Pool level reporting may include volume of advertisements by day
and/or by creative, or some other criterion. Placement level reporting
(e.g., for each publisher flight) may include volume by day and pass-back
percentages. Further, placement level performance (e.g., for each
publisher flight) may include valuation/performance that may be equal to
the difference of the sum of bid values and sum of advertisement costs.
Similarly, the top level domain reporting may include top level domains
with daily and monthly cumulative volume, and daily and monthly
cumulative uniques. The billing reporting for reconciliation for each
publisher flight may include last six months, and month-to-date
information, consumed budget, impressions acquired, calls received,
percentage of pass-back, or some other information. In an embodiment, all
budgets may come from single flight, with definite starts/end dates.
Alternatively, multiple advertisers may start and end campaigns that use
ads from a placement, within the pool start and end dates.

[0273] In another example, there may be a plurality of advertisers
associated with the Global Yield Management system such that there is
optionality from the buyers' perspective (i.e., the buyer may not use
some impression, and may not pay for them). The price to be paid for each
advertisement placement opportunity may be fixed and the advertiser may
have multiple versions of the advertisement that may be used for each
placement opportunity. In this case, the GYM-B 4712 may make decision on,
for example, three dimensions, whether to take the advertisement(s) or
not, which advertisers should take the advertisement or advertisements,
and which creative(s) to show for that advertiser. The optimization may
seek to maximize the sum of a campaign's performance as measured by the
success metric for each campaign. There may be some campaigns for which
the goals may not be completed. This may be considered while setting
priorities by the operator of the GYM-B 4712. The operator of the GYM-B
4712 may have volume goals, which may be taken into account to decide
whether to take an impression or not. Further, the GYM-B 4712 may have
specific performance goals for each publisher associated with the GYM-B
4712, and when those goals are not achieved, it may trigger an automated
email, communicating this to the operator and/or the publisher.

[0274] In another example embodiment, there may be several advertisers
associated with the Global Yield Management system. There may be
optionality from the buyer's perspective (i.e., the buyer may not use
some impression, and may not pay for them). The price to pay for each
advertisement placement opportunity may be variable. The advertiser may
have multiple versions of the advertisement that may be used for each
placement opportunity. In this case, the GYM-B 4712 may decide on
multiple dimensions, for example, whether to take the advertisement (s)
or not, how much to pay for them, which advertisers should take the
advertisement(s), and which creative(s) to be shown for that advertiser,
among others. The optimization may seek to maximize the overall value of
the market by reaching a maximum performance as measured by the success
metric for each campaign for all campaigns linked and by paying the
lowest possible price for each impression. Alternatively, the
optimization may seek to pay impressions `at value` or `at value less
margin`, thereby incentivizing publishers to participate by paying high
prices for selected opportunities. Publishers with high densities of good
opportunities may receive overall higher prices, creating an incentive
for good quality content to participate. In addition, the operator of the
GYM-B 4712 may have volume goals, which may be taken into account to
decide whether to take an impression or not. There may be some campaigns
that may not be able to complete the goals; for them, priorities can be
set by the operator of the GYM-B 4712. Further, the GYM-B 4712 may have
specific performance goals for each publisher associated with the GYM-B
4712, and when those goals are not achieved, it may trigger an automated
email to communicate this to the operator and/or the publisher. It may be
noted that each publisher may optionally specify a `floor price` under
which it may not sell its advertisements.

[0275] Moreover, the above scenario includes multiple advertisers that may
participate from the same GYM-B 4712 system. The RTS 4502 may decide
which advertiser and advertisements to show. The RTS 4502 may have an
organic solution for deciding which advertiser and advertisements to
show. Although the RTS 4502 may not solve publisher pacing, the pool may
decide which advertisement call to use and which to pass-back. The
embodiments of this system facilitate reduction of complexity at the RTS
4502 core and enable a transparent policy facing publishers and publisher
optimizers.

[0276] The functionalities of the GYM-B 4712 system may also include
receiving an advertisement call, translating and calling the RTS 4502,
deciding whether to take the call or pass-back, sending the right answer
(advertisement tag or pass-back address), recording these and other
events processing events using its infrastructure.

[0277]FIG. 46 depicts the temporal relationship between multiple
inventories and advertising campaigns with multiple starting and ending
dates for available budgets. The UI functionality for the GYM-B 4712
system may enable the assignment of a name to a pool and for campaigns
inside the scope of a creating entity (where the pool shows up as an
available inventory source). The UI may also display the budget tab
(e.g., a budget sum of budgets of associated flights). Using the UI, new
flight budgets may be added at any time. In embodiments, multiple flights
may provide budgets and multiple advertisers may be sourced from
inventory.

[0278] In embodiments, budget options may be balanced by allowing only new
flights with corresponding new inventory and matching times and budgets.
A pool may be a `meeting place for exchange` between advertisers and the
pool may be balanced. In other embodiments, budget options may be
balanced by restricting flights and budgets to start/end on a weekly
basis to ensure that the available inventory may be sold each week. It
may be assumed that flight pacing may vary (e.g., if nominal pacing is
USD1K/day, actual may vary from USD0/day to USD3K/day). Further, in
embodiments of the invention, publishers' placements pacing may also
vary.

[0279] The UI may be designed to handle allocation issues across different
pricing frameworks (i.e., fixed or variable mark up percentage) and
different rates that might be paid by advertisers.

[0280] In other embodiments of the present invention, the UI may allow
publishers or advertisers to self-serve. The UI may integrate reporting,
other pricing modalities (variable CPM with floor), other pass-back
mechanisms, and secondary premium, and the like. Pass-back may be resold
as a block or impression by impression.

[0281] In embodiments, an advertisement tag may call a proxy. The call may
include cookie information, agent, and other variables. Javascript, or
some other method, may be used to create the call; the Javascript code
may be served from CDN so that an advertisement tag may be compact and
customized when required. Further, the decision to take or not take
advertisement may happen at the proxy. Using a proxy simplifies the
implementation as it keeps most of the already built bidding
infrastructure intact. Advertisement tag information may be translated
into an RTS 4502 format, for example, by adding a Faux Exchange ID. The
Faux Exchange ID may be unique per advertisement tag. In an embodiment, a
lookup table may be created to categorize inventory, and forward that
information in an RTS 4502 call (e.g. for every impression from XXNews,
Category=News and for every impression for AA, Category=Business).
Moreover, advertisement flights may be targeted at a Faux Exchange ID(s).

[0282] It may be understood that for all the described scenarios herein,
there may be a variant where impressions (that are not used) may be
passed to a secondary buyer, who will take them without the options. This
variant may require the agreement of the publisher, as their
advertisement opportunity will be placed with this secondary buyer. For
scenarios where there is no optionality, the variant may create one.

[0283] In embodiments, use of GYM-B 4712 may facilitate penetration of
advertiser budgets. Advertisers may in turn achieve centralized reporting
and optimization. Advertising agencies may improve campaign performance
by impression inventory allocation. Further, content safety issues with
unknown publishers may be effectively resolved. For cases, where
advertisers negotiate media buy outs and inventory may be sourced from
premium sites or high quality portals; and with a guaranteed budget, the
system may select right advertisement to show for impression. The system
may leverage campaign placements for learning, unify reporting, and
provide early automated reports on publisher performance. For cases,
where publishers execute negotiated media buys and advertisements are
sold to premium brands with protected prices, the system may select a
suitable advertiser and page to show for an impression. The system may
leverage all campaign placements for learning, unify reporting, and
provide automated reports on advertiser performance. Publishers may be
used to deal with ad servers and daisy chains as shown in FIG. 45. The
system may further facilitate the use of an advertisement call that may
send a user browser to an actual ad server to retrieve a graphic or a
redirect that may send a user browser to the next level in the chain.

[0284] In another embodiment, the system may work by selecting the
advertisements to sell, and the minimum price to accept for a bid, and
assigning those advertisements to different buyers. A first buyer may be
an advertisement biddable exchange, a second buyer may be an advertiser,
and a third buyer may be a reseller. Each of the buyers may have
different conditions for buying advertisements, paying premiums in some
conditions, and not taking advertisements in others. One objective of the
GYM-Seller (GYM-S) may be to help the seller to maximize the monetization
of the advertisement inventory sold.

[0285] In one of the implementations, sellers may use the system to send
offers to sell an advertisement(s).

[0286] The GYM-S 4814 system may decide which buyer will get an
advertisement or advertisements, what information to attach to an
advertisement or advertisements, what is the acceptable price to sell,
whether to accept the bid or not, what floor price to be communicated,
whether to offer optionality with the offer to sell, and at what price to
do so, or some other information. The information attached with the
advertisement(s) may vary, and may either include the publisher identity
or may make it anonymous. The system may keep a record, and may respect
rules about which advertiser(s) are allowed for each publisher and vice
versa.

[0287] In an example, there may be a single seller and a single buyer
associated with the Global Yield Management system. There may not be
optionality from the buyers' perspective. All calls with advertisement
opportunities from seller may be responded by the buyer with an
advertisement bid. Similarly, there may not be optionality from the
seller's perspective such that all bids sent by buyers may be accepted.
The price that is bid for each advertisement placement opportunity may be
fixed i.e., all bids may be at the same fixed price. The advertiser may
have multiple advertisement sizes and a page may be sent to the buyer.
This page may be a part of the other pages provided by the publisher, or
it may belong to a specific category of content. In this case, the GYM-S
4114 may decide in only one dimension (e.g., advertisement size) to be
sent. In the case where there is no signal from the buyer to the seller
indicating which inventory performs better, the optimization strategy may
be to send advertisement opportunities with the lowest possible
alternative monetization to the buyer. However, in the case where there
is a signal that indicates what advertisements perform better, the
strategy may be to maximize performance by sending the highest performing
pages with the lowest possible alternative monetization.

[0288] In embodiments, the GYM-S 4114 may have specific monetization goals
(revenue per thousand advertisements sold) for each publisher associated
with the GYM-S 4114, and when those goals are not achieved, it may
trigger an automated email, communicating the operator and/or the
advertiser of this fact.

[0289] As another example, there may be a single seller and multiple
buyers associated with the GYM-S 4114 system. There may not be
optionality from the buyers' perspective. All calls with advertisement
opportunities from seller may be responded by the buyer with an
advertisement bid. Similarly, there may not be optionality from the
seller's perspective such that all bids sent by buyers may be accepted.
The price that may be bid for each advertisement placement opportunity
may be fixed (all bids may be at the same fixed price). The advertiser
may have multiple advertisement sizes and a page may be sent to the
buyer. This page may be a part of other pages provided by the publisher,
or it may belong to a specific category of content. In this case, the
GYM-S 4114 may decide on dimensions, such as, advertisement size, a page
to be send, and buyer to send it to. In the case where there is no signal
from the buyer to the seller indicating which inventory performs better,
the optimization strategy may be to send advertisement opportunities with
the lowest possible alternative monetization to the buyer. However, in
the case where there is a signal that indicates which advertisements
perform better, the strategy may be to maximize performance by sending
the highest performing pages, with the lowest possible alternative
monetization. GYM-S 4114 may have specific monetization goals (revenue
per thousand advertisements sold) for each publisher associated with the
GYM-S 4114, and when those goals are not achieved, it may trigger an
automated email, communicating the operator and/or the advertiser of this
fact.

[0290] In other example, there may be a single seller and multiple buyers
associated with the GYM-S 4114. There may not be optionality from the
buyers' perspective. All calls with advertisement opportunities from the
seller may be responded to by the buyer with an advertisement bid.
Further, there may be optionality from the seller's perspective (e.g.,
not all bids sent by buyers may be accepted). The price that is bid for
each advertisement placement opportunity may be fixed (e.g., all bids may
be at the same fixed price). Furthermore, the publisher may have multiple
pages, each with different types of content and each with multiple ad
sizes available for ads placement; the publisher can decide which
specific page to send to the buyer, and within that page, which ad size
to send. In this scenario, the GYM-S 4114 may decide in dimensions, such
as, advertisement size and page to be sent, buyer to send it to, and
whether to accept the resulting bid. In the case where there is no signal
from the buyer to the seller indicating which inventory performs better,
the optimization strategy may be to send advertisement opportunities with
the lowest possible alternative monetization to the buyer. In the case
where there is a signal that indicates what advertisements perform
better, the strategy may be to maximize performance by sending the
highest performing pages, with the lowest possible alternative
monetization. The GYM-S 4114 may have specific monetization goals
(revenue per thousand advertisements sold) for each publisher associated
with the GYM-S 4114; and when those goals are not achieved, it may
trigger an automated email, communicating the operator and/or the
advertiser of this fact.

[0291] In another sample embodiment, there may be a single seller and
multiple buyers associated with the GYM-S 4114. There may be optionality
from the buyers' perspective. For example, not all calls with
advertisement opportunities from a seller may be responded to by a buyer
with an advertisement bid. Similarly, there may be optionality from the
sellers' perspective; not all bids sent by buyers may be accepted. The
price that may be bid for each advertisement placement opportunity may be
fixed. Further, the advertiser may have multiple advertisement sizes and
a page may be sent to the buyer. In this case, the GYM-S 4114 may decide
in dimensions, such as, advertisement size and a page to be sent, the
buyer to whom the page may be sent, and whether to accept the resulting
bid. The system may utilize a "no bid by buyer" signal to measure the
level of interest in inventory, and it may send pages with the highest
likelihood of getting a bid, and with the lowest possible alternative
monetization. The GYM-S 4114 may have specific monetization goals
(revenue per thousand advertisements sold) for each publisher associated
with the GYM-S 4114, and when those goals are not achieved, it may
trigger an automated email, communicating the operator and/or the
advertiser of this fact

[0292] In another example, there may be multiple sellers and multiple
buyers associated with the GYM-S 411. There may be optionality from the
buyers' perspective. For example, not all calls with advertisement
opportunities from a seller may be responded to by the buyer with an
advertisement bid. Similarly, there may be optionality from the seller's
perspective; not all bids sent by buyers may be accepted. The price that
is bid for each advertisement placement opportunity may be fixed (all
bids may be at the same fixed price). The advertiser may have multiple
advertisement sizes and a page may be sent to the buyer. In this case,
the GYM-S 4114 may decide in dimensions, such as, which seller to use,
which advertisement size and page to send, which buyer to send it to, and
whether to accept the resulting bid. The system may take advantage of the
"no bid by buyer" signal to measure the lack of interest in inventory,
and it may send pages with the highest likelihood of getting a bid, and
the lowest possible alternative monetization. The GYM-S 4114 may have
specific monetization goals (revenue per thousand advertisements sold)
for each publisher associated with the GYM-S 4114, and when those goals
are not achieved, it may trigger an automated email, communicating the
operator and/or the advertiser of this fact.

[0293] In another example, there may be multiple sellers and multiple
buyers associated with the GYM-S 4114. There may be optionality from the
buyers' perspective. For example, not all calls with advertisement
opportunities, from seller, may be responded by the buyer with an
advertisement bid. There may be optionality from the seller's
perspective; not all bids sent by buyers may be accepted. Further, the
price that is bid for each advertisement placement opportunity may be
variable. The advertiser may have multiple advertisement sizes and a page
may be sent to the buyer. In this case, the GYM-S 4114 may decide in
dimensions, such as, which seller to use, which advertisement size and
page to send, which buyer to send it to, and whether to accept the
resulting bid. The system may utilize the "no bid by buyer" signal, and
the price bid signal to measure the level of interest in inventory, and
it may send pages with the highest likelihood of getting a bid and with
the lowest possible alternative monetization. The GYM-S 4114 may have
specific monetization goals (revenue per thousand advertisements sold)
for each publisher associated with the GYM-S 4114, and when those goals
are not achieved, it may trigger an automated email, communicating the
operator or the advertiser of this fact.

[0294] FIGS. 47 and 48 are schematic representations of an exemplary GYM
for buyers and sellers using a proxy translator in real time bidding
calls, in accordance with an embodiment of the present invention.

[0295] FIG. 49 depicts another schematic representation of an exemplary
GYM for sellers using real time bidding system for valuation, in
accordance with an embodiment of the present invention.

[0297] In embodiments, an agency data campaign descriptor may describe the
channels, times, and budgets that may be allowed for diffusion of
advertising messages. Agency data historic logs may describe the
placement for each advertising message to a user, including, for example,
one or more of a user identifier, the channel, time, price paid,
advertisement message shown, and user resulting user actions. Additional
logs may also record spontaneous user actions. Advertiser data 152 may
include, but is not limited to, business intelligence data that may
describe dynamic or static marketing objectives (e.g., the amount of
overstock of a given product that the advertiser has in its warehouses.)

[0298] Key Performance Indicators (KPI) may be the set of parameters that
express the `goodness` for each given user action. For example, product
activation may be valued at some specified price X, and a product
configuration can be valued at a different price Y The KPI will be
expressed as the sum of these different campaign goals (in this example:
product activation, and product configuration), each with specific
weights.

[0299] Historic event data 154 may be significant since the real time
bidding system may attempt to correlate the time of user events with
other events happening in their region. For example, response rates to
certain types of advertisements may be correlated to stock market
movements. Historic event data 154 may include, but is not limited to,
weather data, events data, or local news data. User data block may
include data provided by third parties that may contain personally linked
information about advertising recipients. This information may show users
preferences or other indicators that label the users. Further, a
contextualizer service may identify the contextual category of a medium
for advertising. For example, a contextualizer may analyze the web
content to determine whether a web page contains content about sports,
finance, or some other topic. This information may be used as input to
the learning system, to better refine which advertisements may appear on
which types of pages. Real time event data may include data similar to
historic data, but is up to date (e.g., for seconds, minutes, hours, or
days). For example, if the learning machine facility 138 identifies a
correlation between advertisement performance and historic stock market
index values, the real-time stock market index value may be used to value
advertisements by the real time bidding machine facility 142. Examples of
advertising distribution services may include Ad Networks, Ad Exchanges,
Sell-Side Optimizers, and the like.

[0300] The advertising recipient may be a person who receives an
advertising message. The content may be specifically requested ("pulled")
as part of or attached to content requested by the advertising recipient,
or "pushed" over the network by the advertising distribution service.
Some non-limiting examples of modes of receiving advertising may include
the Internet, mobile phone display screens, radio transmissions,
television transmissions, electronic bulletin boards, printed media, and
cinematographic projections.

[0302] An operator of GYM for Buyers (GYM-B 4712) may create placements
for each publisher that it may intend to associate with. Each of these
placements may have several parameters. The operator or an agent may
negotiate to buy media under certain conditions with a publisher. The
publisher and operator may agree on a certain number of impressions,
price to pay, and whether there is the opportunity of not using some
impressions. In some cases, the price to pay may also be left undecided.
In an embodiment, the publisher may call the GYM-B 4712 whenever an
advertisement opportunity appears. The GYM-B 4712 may decide which
advertisement to use and in some cases, which advertiser should use the
advertisement, whether the impression is used, and how much to pay for
it. In order to decide, the GYM-B 4712 may use multiple constraints,
including the value of the advertisement to each advertiser, the pacing
of the publisher relative to goal, the pacing of the advertiser campaign,
whether the consumer has reached its frequency limit, and whether the
operator is able to use publisher media for a given advertiser. Once a
decision is made, the GYM-B 4712 may send a call to an advertising
distribution service to deliver the advertisement. In a case where the
impression is not to be used, the GYM-B 4712 may re-sell it to a
secondary market or return it to the publisher for the publisher to use.

[0303] The GYM-B 4712 may keep track of impression calls received through
each publisher deal, such as the values of these opportunities, whether
it was taken or not, and which advertiser and creative took it.
Statistics may be created to depict which publisher deals are more
valuable than others, how many times advertisement impressions where
rejected/taken, and which advertisers or creative(s) are using the
impressions for a given publisher. The GYM-B 4712 may also provide
analytics at the page level of the significantly effective pages for each
publisher, thereby providing an input to the publisher about what content
is most effective. Reporting created from the GYM-B 4712 may be used to
bill the advertiser about the media used, and to correlate bills received
from publishers with actual media consumed by the advertisers. Moreover,
statistics about performance by publisher may be used to trigger
automated email messages to the operator, publisher or both when certain
conditions are met.

[0304] The GYM-S 4814 may maximize benefits on behalf of publishers, in
accordance with an embodiment of the present invention. The GYM-S 4814
may work on behalf of one or many publishers, and be associated with
several advertisers. The operator of the GYM-S 4814 may create placements
for each advertiser and publisher it may intend to associate with. An
operator or an agent may negotiate to buy media under certain conditions
with one or more buyers. The buyer and operator may agree on certain
number of impressions, price to pay, and whether there is the opportunity
of not using some impressions. In some cases, the price to pay may also
be left undecided. The GYM-S 4814 may assign each advertisement
opportunity to an advertiser that may maximize the monetization on behalf
of the publisher. An estimation regarding this may be created by querying
an instance of the real time bidding system that may include valuation
frameworks for participating advertisers. These frameworks may have been
created using machine learning, including the machine learning and
analytic platform depicted in FIG. 1A, that takes into account each
advertiser campaign KPI. The GYM-S 4814 may decide which advertisement to
use and in some cases, whether the impression may be used, which
advertiser should use it, and how much to be paid for it. For this
purpose, the GYM-S 4814 may use multiple constraints, including the value
of the advertisement to each advertiser, the pacing of the publisher
relative to goal, the pacing of the advertiser campaign, whether the
consumer has reached its frequency limit, whether the operator is able to
use publisher media for a given advertiser, and what the alternative
realization price is for such advertisement with other advertisers. Once
a decision is made, the GYM-S 4814 may send a call to the advertiser's
advertisement distribution service to deliver the advertisement, or if
the impression is not to be used, it may re-sell it to a secondary market
or return it to the publisher for the publisher to use.

[0305] In embodiments, the GYM-S 4814 may keep track of impression calls
received from each publisher and delivered to each advertiser, how much
each of these opportunities was valued, whether it was taken or not, and
which advertiser and creative took it. Therefore, statistics may be
created to show which advertisers are more valuable than others, how many
times advertisement impressions were rejected/taken, and which
advertisers or creative(s) are using the impressions for a given
publisher. The GYM-S 4814 may also provide analytics at the advertisement
message level of the most effective advertisers for each publisher (most
valuable); thereby providing an input to the publisher about what content
is most effective. Reporting created from the GYM-S 4814 may be used to
bill the advertiser about the media used, and to correlate bills received
from publishers with actual media consumed by the advertisers. Moreover,
statistics about performance by publisher may be used to trigger
automated email messages to the operator, publisher, advertiser or to
some or all of them, when certain conditions are met (e.g., in cases
where media received is less than the requirement in a given period,
media received was underperforming, media more than the requirement was
sent, contract is about to finish, advertiser advertisements are
underperforming, etc.)

[0306] The present invention facilitates real time optimization for online
media acquired with negotiated deals and with fixed conditions. The real
time optimization for online media may be sold with negotiated deals and
with fixed conditions. The present invention further facilitates managing
yield of such media, across multiple advertisers and using a simple to
use integration system. Similarly, the present invention facilitates
managing yield of media across multiple publishers, using real time
bidding system.

[0307] In an embodiment of the present invention, a real time bidding
system to decide on advertisement value may be used. In another
embodiment of the present invention, a dynamic pricing adjustment that
may trade negotiated media and exchange media for each advertisement
opportunity may be used. In yet other embodiment, a dynamic pricing that
may trade publishers in real time to monetize content effectively may be
used. The present invention may facilitate creation of a market across
publishers' negotiated deals that may compete for the budget of all
available advertisers and creation of a market across advertiser
negotiated deals, which may be traded in real time for impressions
available from publishers. Further, the present invention may facilitate
reduction of waste, since the maximum number of advertisements per
consumer may have reached for one advertiser, but another one may be able
to use the impression with benefit. The present invention may be use to
create an early alert system that may communicate to publishers,
advertisers, operators or a combination of them when media acquired
through negotiated deals or advertisements placed may be underperforming
relative to goals or past performance, or when the media may be out of
the pre-negotiated parameters (impressions per day, etc.).

[0308] In accordance with various embodiments of the present invention, a
system for multi-channel decisions for acquiring media for placing
advertising may be executed in real time (such as an acceptable time
constraint, which may depend on the media channel where the media is
acquired). Examples of the channels upon which the multi-channel
decisions may be made may include online display advertising, mobile
display advertising, online video advertising, online search advertising,
email advertising, TV advertising, cable advertising, Addressable IP-TV
advertising, Radio advertising, Newspaper advertising, Magazines
Advertising, Outdoor advertising, and the like.

[0309] The system may use a uniform framework to decide where to place
advertisements across multiple channels, including those described above.
The uniform framework may assign a value to each advertisement
opportunity, and may decide on the message to be presented to the
consumer. The framework may provide valuation to single advertisements
and to a set of advertisements. Further, the system may automatically
adjust media plans to execute campaigns by assigning a lower value to
advertisements that may be less effective, which may either force the
seller to lower their prices or not sell at the offered price. Sellers
may make their advertisement opportunities attractive by lowering the
prices. On the other hand, by not accepting to sell, they may drive a
budget reallocation to other effective advertisement opportunities. In
both cases, the valuation function may define the media plan, may adjust
buying volumes, and reallocate budgets.

[0310] The framework of the present invention, include the learning
machine and analytic platform depicted in FIG. 1A, may be used to
describe multiple channels; therefore, these changes may trade off one
channel against another. As the framework is constantly refreshed, the
framework may constantly adjust how each channel is used and how they
interact based on results. This may subsequently result in the selection
and tradeoff of the best way to reach consumers across all media
channels. The framework may be represented, for example, as a
mathematical function or an algorithm, with multiple variables as input
and one or many variables as output. The input of the mathematical
function may include parameters that describe "Ad Placement
Opportunities" (APO). For example, the mathematical function may receive
input variables such as "time of day" for placing the advertisement 5002,
"geographical region" where the consumer is located 5004, "type of
content" on which advertisement may be inserted 5008, "size of the online
advertisement" that may only be valid for online display advertisements
5010, "length of the TV spot" that is only valid for TV advertisements
5012, "print advertisement size" 5014, "odd or even page" that is only
valid for print advertisements 5018, "channel used" that tells the
mathematical function about the type of advertisement placed 5020,
"consumer ID" that can be an actual consumer ID or a Virtual Global
Consumer ID 5022 as shown in FIG. 50. Additionally, the input variables
may be "impressions" that may describe the size of the purchase in number
of messages delivered, "number of consumers" that may describe the size
of the purchase in number of consumers impacted, and "budget" that may
describe the size of the purchase in monetary value. The list of the
input parameters is exemplary and there may be other input parameters
that may be involved in a framework for an advertisement campaign with
three channels such as online display, TV, and print.

[0311] Considering an example where a TV spot may be evaluated by the
system, the input parameters "time of day", "geographical region", and
"type of content" may not be provided. In this scenario, the mathematical
function may be able to provide an answer in cases where parameters are
not provided, assuming a typical distribution for each of the parameters.
Similarly, parameters "size of online advertisement", "odd or even page",
and "consumer ID" may not be applicable. The mathematical function may
ignore the fact that these parameters may not be relevant in this
context. However, parameters "length of the T.V. spot" and "channel used"
may be available and may also be used. Parameters "impressions", "number
of consumers", and "budget" illustrate the size of the decision, and at
least one of them may be provided. As a consequence, each combination of
parameters (variables) describes an "Ad Placement Opportunity" (APO). The
combinations that may not be feasible (e.g., TV advertisement with "odd
or even page" value), may not create a valid APO. The output of the
mathematical function may at least be a "value" for the advertisement
opportunity, either as an index, or as a monetary value. Additionally,
the system may help select the message to show through one or more
additional output variables that can describe the message. Examples may
include concept of the advertisement to use from a list of concepts, the
variation of the advertisement to use from a list of available
variations, and the call to action of the advertisement to present to the
consumer from a list of available CTA. Mathematically, it may be
represented in one embodiment as is listed below: [0312]
advertisement(value,concept,variation,CTA)=f(TOD,GEO,TOC,SIZE,Length,Oor
E, Chan, ConsID,Imp,NofCons,Budget)

[0313] In embodiments, the APO and message shown may impact consumers and,
subsequently, influence the valuation and output message from the
framework. The impact on consumers may depend on the nature of the
advertisement campaign, the brand, and the advertising market. Therefore,
the output of this framework may be different for each campaign and
market state. As a consequence, a new framework may be created for each
campaign. This may be significant since the campaign may be adjusted to
impact consumers using different combinations of variables (see FIG. 51).

[0314] Further, the framework for the valuation may be created by using
machine learning techniques, as describe herein and including the
facilities depicted in FIG. 1A. These machine learning techniques may
rely on a closed feedback loop that may show messages through APOs to
consumers, and capture data on how those users have modified their
behavior as a consequence of these APOs and messages. The framework
created by machine learning techniques may assign APOs and messages with
higher probabilities to influence consumers in positive way versus other
messages with a lower probability.

[0315] Owing to the nature of the advertising market, different channels
may be expected to have different degrees of coarseness on their
addressability. For example, while it is possible to buy a single APO for
online display, TV APO may be sold through whole blocks that may involve
multiple advertisements that may be presented to a large audience. The
framework, as described above, may evaluate APO in the unit in which they
are purchased, using averages and other statistics to estimate values for
channels that have a coarse addressability. For example, outdoor
advertising may be traced to people living or working in several
zip-codes, their number, and the zip-codes to which they belong. In order
to measure the results of each APO and message shown, it may be linked to
an advertiser's results for each APO and the message's ability to improve
them. Subsequently, the advertisers may use these measurements to modify
their campaigns to maximize the effect of their advertisement messages.

[0316] In an embodiment, online advertising may use unique numbers, stored
in cookies, to anonymously identify consumers and link APOs used and
messages shown to consumers. However, even when these consumer's unique
numbers are anonymous, there may be cases where use of these unique
numbers may not be recommendable or possible. In such cases, the use of
certain characteristics of the APO description may help to establish a
link with consumers. For this purpose, small segments of relatively
homogeneous consumers may be described by some APO variables. For
example, at a certain time of day, a certain geographic region, and
consumer's interest in a type of content, a set of individuals may be
defined that may constitute a Synthetic User Identifier (SUID)

[0317] In another embodiment of the present invention, the effect of APOs
and messages shown to these groups of consumers (described by their CID)
may be linked to actual results through a probabilistic matrix M. This
concept may be useful for cases where it may not be possible to address
advertisements to individuals, or to follow individuals across channels
(e.g., cases involving multiple channel advertising, TV advertisings, and
print and online media advertising). The methodology to create this
probabilistic matrix may be based, at least in part, on the minimization
of errors. Each row in the matrix may codify a linear combination of
weights that may translate strength of messaging through APOs and
messages into actual results that may be measured. The coefficients of
the linear combination may be changed to minimize the error between what
the linear combination states as result, and the actual result. Further,
the framework may also consider the concept of a consumer journey, from
initial awareness about a brand to an actual conversion at, for example,
an advertisers' store. Consumer journey may refer to different states a
consumer may pass through the process of buying. It may be the objective
of every advertisement campaign to influence consumers to move along this
journey, even in cases where an actual conversion at the advertiser's
store occurs outside the timeline is being measured.

[0318] In an embodiment, the framework may use the measurements along the
consumer's journey as input to sense the buying behavior of consumers and
understand the effect of APOs and messages on changing such a
state/behavior. This may be significant in case of multiple channels, as
a few channels (such as TV and radio) may influence consumers effectively
in the initial steps of their journey, and others may influence during
the advanced states, helping to close the sale (such as display and
search advertisements). The consideration of the consumer journey may
result in providing a more accurate valuation of each APO. By measuring
the consumer's progress in the journey, and using this data as input to
the framework, it may be possible to provide a more effective valuation
of APOs and messages. However, a few channels may have a relatively small
effect in driving consumers through the final states, but may be
significantly valuable in driving consumers in the initial states.

[0319] In embodiments of the present invention, there may be a number of
internal and external machines and/or services in the system and an
interaction among them may result in effective real time bidding for
advertising delivery. For example, an advertiser may place an "order"
with instructions limiting where and when an advertisement may be placed.
The order may be received by the learning machine facility 138.
Thereafter, the advertiser may specify the criteria of `goodness` for the
campaign to be successful. Such `goodness` criteria may be measurable
using the tracking machine facility 144, or through other external
systems, such as surveys. In addition, the advertiser may specify
channels to use, and may provide messages. Further, the advertiser may
provide historic data to bootstrap the system.

[0320] Based on the available data, the learning system may develop a
framework for valuation, which can be codified as a mathematical
function. The function may calculate the expected value of each
advertisement placement opportunity, and may also provide the concept,
variation, and call to action among others, to select the message to show
to consumers. The selection of value and message to show may maximize the
specified `goodness` criteria. Thereafter, the mathematical function may
be received by the real time bidding machine facility 142. Bid requests
may be received by the real time bidding machine facility 142 and may be
evaluated for its value for each advertiser, using the received
algorithms. Subsequently, bid responses may be sent for advertisements
that may have an attractive value. The selected advertisement may then be
placed at a particular price.

[0321] In an embodiment, the mathematical function may also be invoked
through a manual process, specifying the value for each variable that
describes the advertisement placement opportunity to evaluate. In both
cases, one or many advertisements may be valued simultaneously. As a next
step, a matrix may be created that may link advertisement placement
opportunities and messages shown to results, either purchases or change
in consumers' buying behavior. The advertisement result linking matrix
may be created and constantly adjusted for tracking the results that
cannot be tracked for each consumer.

[0322] In an embodiment, advertisements may be tagged with a tracking
system, such as a pixel displayed in a browser. The tracking machine
facility 144 may log advertisement impressions, user clicks, and/or user
actions. Also, additional external metrics that involve consumer state
may be included. The results, advertisement placement opportunities, and
messages may be linked through the advertisement result linking matrix.
The `goodness criteria` may be used by the learning system to further
customize the valuation mathematical function. The system may also
correlate expected values with current events in the advertisement
recipient's geo-region.

[0323] The various embodiments of the present invention facilitate
allocation of budget for media and pricing. The budget may be updated in
real time (e.g., in a timely way for taking a decision as the channel
requires it). The present invention may enable the use of a single
framework to decide on value and message across multiple media channels,
thus enabling trading advertisements shown through one channel with
advertisements shown through a different channel. Further, varying
degrees of coarseness in the type of decision may be involved to acquire
media. Therefore, coarseness may be determined by the addressability,
type of media, and the granularity that may be achieved at expressing the
effect of advertisements.

[0324] The present invention may facilitate optimization of the effect of
advertisements by paying the right price and ensuring advertisements are
placed to the consumers and through the channels that ensure their best
effect. Still further, the present invention considers the state in which
the consumer is as they progress in the journey from initial awareness to
purchase of a good or service. Measurement of consumers' buying behavior
through surveys or panels may also be performed; this measurement is
independent of the fact that whether they purchased a good or service. In
addition, the present invention facilitates use of a probabilistic
approach for linking different channels, and their results as a change in
consumers' state and purchases of goods or services. This approach may be
used in cases where there is little or no certainty to link individuals
and results.

[0325] In embodiments, the present invention may provide for impression
level decisioning for guaranteed buys towards audience optimization.
Referring to FIG. 52, the system may apply rules in real-time to allocate
impressions to best advertisement (advert') campaign, such as based on
consumer segment membership. For example, and as depicted, various
context sources (e.g. CNN.com, Vanityfair.com, espn.com, vogue.com) may
be presented with an opportunity to place an advert, such as to
individuals in a certain demographic, individuals with a known profile,
in relation to a creative (e.g. AXE, Dove, Vaseline), and the like. The
use of machine learning or statistical techniques may be utilized to
identify segment fitness, such as in cases where the profile of the
consumer behind an impression is unknown. The regulation of the tradeoff
between segment fitness and campaign pacing may be through a coefficient.

[0326] In many cases, advertisers may be interested in showing their
advertisements within a specific online publisher media. In these cases,
the advertiser may buy 100% of the advertisements shown within this
online publisher. The selection of which advertiser to buy may be guided
by the audience that predominantly browses the website. In other cases,
advertisers may be interested in showing their advertisements using a
combination of online and offline content channels, such as online
websites, online mobile, online video, TV, IPTV, print, radio, and the
like. In these cases, the minimum investment size may vary by channel,
and outlet, but it may be in most cases possible to know certain
attributes for the addressed audience. For example, 60% of the consumers
browsing at a sports site may be male. Advertisers, seeking to target a
male audience, may show advertisements at this sports site, and consider
those advertisements shown to women, to not hit their target, but still
be paid for. As such, the effective cost per thousand advertisements
shown in the target may be higher by a certain factor that incorporates
the spill over outside the target audience. In many cases, a product may
target several audiences, some of which may be primary, and others may be
secondary. With more advanced technology it may now be possible to know,
such as in a percentage of cases, what is the profile of a consumer, so
as to know if the consumer is `in target`. When an advertiser seeks to
advertise different products with non-overlapping audiences, the system
may be able to identify users as they arrive, as part of a segment or
another, and then show the most appropriate advertisement for the most
appropriate product. By doing this, the system may reduce the spill over,
using those impressions from the sports site that are shown to women, to
show advertisements relevant to women. In embodiments, this may be
limited to an individual on which there is data to identify their
profile.

[0327] In some cases the ability to address specific impressions may not
be available (e.g. broadcast TV, radio), and the spillover may be
unavoidable. However, the system may still create an effective cost
including the spillover. The system may compare the efficiency of the
channel with other channels, using the analytic platform as described
herein, where more granular addressability is available. In certain cases
the same channel may provide diverse levels of granularity and variable
price associated with each. For instance, a TV network may sell `daily
rotation national broadcast` advertisements at one price, `prime time
national broadcast` at a higher price, `prime-time regional broadcast` at
a different price, and `specific show national broadcast` at a different
price as well. The platform may evaluate each target audience, and
compare them against all other available ways to reach the target
audience. Moreover, the system may detect whether it needs to complement
one channel with a different channel, for example, expanding the number
of consumers reached with an TV broadcast offer, with individuals found
online, that belong to the same target segment. In order to measure
overlap between these two segments, surveys or other methodologies may be
used. Further, the system can create a score for every consumer, as to
whether they belong in a segment or not. This score may be created using
machine learning techniques, or other statistical techniques, the
analytic platform, as described herein, and/or use information from
multiple sources. One source of such information may be related to the
consumer, such as past browsing history for the consumer, exhibiting the
interests the consumer has, collected online or from off-line behavior
matched to an online ID, demographical, geographical, behavioral or other
information related to the consumer. The system may also consider the
types of `creatives` the consumer likes or dislikes, and which ones the
consumer has interacted, such as described herein.

[0328] Another source of such information may be related to the context
where the ad will be seen, and may include the type of channel used, such
as online video, online mobile, online text, television, interactive
television, IPTV, physical newspapers, physical magazines, radio, and the
like. For any content, no matter what the channel is, it may be topically
categorized (e.g., sports, news, science, entertainment), thus
information about the topical content may also be used. For any content,
there may be a brand for the specific content (e.g., a specific piece of
news or science published on the web, a show name when broadcasting
through TV). Content brand may be information that can be used as well.
At the same time there may be a publisher name, and families of
publishers, which groups have certain specific contents, in a
hierarchical manner. For example in TV, it may be the channel name
(ESPN2), and the network name (ESPN), besides the specific show name,
such as when considering online sites there are specific web pages, that
belong to a section of a website, that belong to a website, where such a
website may in turn belong to a publisher. For every content there may
also be additional qualifiers, such as whether it is paid or free, user
generated content, broadcast, editorialized; whether it is public air
broadcast, or cable; high definition or standard definition; stereo,
multichannel or mono; color or monochrome; and the like.

[0329] Another source of such information may be the creative, which
denotes the specific advertising message that is shown to a consumer. Any
information that describes the creative can be used. The creative may be
described by its nature as static display, animated or dynamic display,
motion picture, audio, and the like. The creative may be described by its
size, such as in pixels, seconds, column-inches, column-cms, and the
like. The creative may be described by its intent in trying to show
product features, interest consumers with a low price, engage with the
consumer at an emotional level, explain to the consumer advantage over
competitors, explain to the consumer why competitors are not adequate,
and the like. The creative may be described by its specific message. The
creative may be described by its success, and where, when and with whom
such success happened, and how was it measured. The creative may be
described by the time it has been shown to consumers.

[0330] A score may exist for every consumer, and for every impression, not
only for those consumers whose profile is known. The score may be higher
with a higher certainty that the consumer is a member of a certain class
or has certain attributes. The system may describe it as the likelihood
of having a certain `some`-ness, an example of which may be `urbanicity`
(likelihood of living in a urban environment), `rational`-ness
(likelihood of thinking like a rational thinker), `female`-ness
(likelihood of behaving like a female), and the like. For example, the
score may describe the probability of an individual being a member of a
marketing segment. This score may change by the closeness of the
individual to the description of the attribute. For example, someone
living in a suburb has a higher `urbanicity` score than someone living in
a `deep rural` geographical location. The score may change with
additional data that further confirms the individual's score, such as
knowing only roughly the region where an individual resides, only by
itself, will project a certain average `urbanicity` value on that
individual; knowing the specific area where the individual resides allows
to further refine the value of such score, and the like. The geographic
region may be just one of the parameters used to estimate someone's
`urbanicity`; others may be the type of content visited.

[0331] By using this score, the system may allocate consumers to the
segment that best fit them, even when their profile is not known. The net
result may be that every impression will be used to the best possible
application. For people for whom the profile is known, the system will
allocate them to a segment or segments they are members of; for people
with an unknown profile, scores for every profile may be used. This score
may be used in combination with another score that reflects the campaign
need to deliver advertisement impressions in time. Campaigns that have
delivered enough impressions may have a lower score vs. campaigns that
are short of their goals. These two factors may be combined so that
campaigns run within their expected impression delivery rate, and with
the best possible consumer fit. Allowing campaigns to over or under
deliver may allow for considering better segment fitness coefficients.
Thus the weighting used to combine the coefficients in the previous row
may drive the tradeoff between segment fitness and campaign pacing. A
third party system may then measure the audience that received
advertisements and verify whether they were in a target audience, such as
using a recruited panel methodology. For instance, such a third party
system may recognize that the execution delivered the highest effective
cost per thousand advertisements delivered, for a campaign, measuring
effective cost per thousand, as counting only those advertisements
delivered to an `in-target audience`, considering the media and data cost
associated with the campaign, and the like.

[0332] By using a methodology as described herein, it may be possible to
achieve a global management of the yield of the content used to show
advertisements. In many cases, the buyer of content to show
advertisements may be corporations with multiple divisions, associations
of corporations, and the like, willing to share in a cooperative. Within
a corporation its divisions may have different lines and products, and
for each product and line there may be different messages, creatives,
offers, and the like. By using the system described herein, it may be
possible to maximize the effect of a given investment in content to show
advertisements. Each advertisement may be selected as the best match to
the advertising goals of the advertiser, the effect of advertising, given
the constraints of using a specific investment in content to show ads,
given the constraints of minimum and maximum investment levels per
corporation, division, line, product, message, creative, offer, and the
like. The search of such optimal allocation may incorporate the nature of
the content being acquired, be it on an impression-by-impression basis or
on a specific minimum addressable investment size.

[0333] In embodiments, the present invention may provide for methods and
systems to maximize advertisement effectiveness based on automated
incorporation of off-line results, where the system may receive real time
feedback from an offline source (e.g. surveys, offline purchase patterns)
and incorporates such feedback into the optimization of an advertisement
campaign. The system may utilize the differential between exposed and
unexposed populations, across combinations of attributes; refine the
inventory of advertisements used for brand metrics oriented advertising;
provide measurement of cost per newly aware person, newly favorable
person, people newly considering brand for purchase; optimize an
advertisement campaign towards the lowest cost per newly aware; and the
like. Referring to FIG. 53, a bid request may be related to bit request
valuation, bid response, real time bidding (RTB) exchanges, and
optimization parameters. FIG. 54 shows an embodiment of a process flow
from an RTB branding bidding function, to a campaign, survey, responses,
and valuation algorithms leading to an optimization engine. FIGS. 55-56
illustrate embodiments of how exposed market increments may be adjusted
as survey results tally from a campaign.

[0334] When placing advertisements to consumers, one of the possible goals
of such advertisements is to influence consumers' awareness about a
product or message, to increase favorability or ensure the product is
within a consideration set. These are generally referred to as `branding
metrics`. In these cases it is desired to measure results through surveys
to such consumers, in such a way that the results of showing those
advertisements can be measured. In certain cases, the population of
consumers would be divided in two, with one part of the population shown
actual advertisements (exposed), and the other part of the population
shown advertisements for a different brand, advertisements about a
non-profit organization, and the like, or no advertisement at all
(unexposed). Surveys to measure branding metrics are provided to both
groups, exposed and unexposed. It is expected that people exposed to
advertisements would respond to the survey with a higher amount of the
relevant brand metric, than people unexposed. This differential is
referred to as absolute brand lift, and describes the incremental in the
brand metric as a result of ad exposure. Further, it may be expected that
within the people in the exposed condition, those exposed to, for
example, particular contents, times of the day, or from some specific
regions, would exhibit an even higher absolute brand lift than others.
Attributes such as these, alone or in combinations, describe areas of the
advertisements inventory where the system was most effective finding a
receptive audience to its advertisements. These attributes may be in the
hundreds, and may vary amongst different types of advertisement. For
example, attributes may belong to various classes, such as those that
describe the consumer receiving the advert, those of the inventory used
to deliver the advert, those relevant to the advert shown (size, concept,
color), and the like.

[0335] The system may autonomously decide to be more proactive to acquire
such areas of the advertisements inventory, such as through higher bids
in a real time environment, through reporting that can be translated into
orders to buy, and the like, in a non-real time environment. The
optimization methodology may opt to seek the highest possible brand
metric, to seek the highest possible differential between an exposed and
an unexposed population, to achieve the most effective incremental brand
lift, and the like. Despite the highly dynamic nature of advertising,
where consumers are ever changing preferences, the system may provide
advice that may dynamically adjust its bidding behavior, so as to best
capture the results offered by optimization, to continuously incorporate
survey responses, to enable the creation and refine a model for driven
brand metrics, and the like. Such an automated system may detect where it
can be most effective as described herein, and decide what ad to show to
each consumer, and within each context, to maximize the relevant brand
metrics. Such an automated system may also work with exchange tradable
media, advising how much to bid for each individual impression, such as
based on the underlying value of each individual impression.

[0336] In embodiments, objectives and metrics to measure as system output
may include maximum brand lift, the number of newly aware people, an
estimate value for making a consumer newly aware, and the like. While
surveys are one type of off-line metric that may be incorporated, other
metrics such as sales of products may also be used. In this alternative
use, the system may receive information about consumers buying products,
creating a pattern of purchase across people exposed to ads, people
unexposed to advertisements, and the like. The difference in purchase
patterns between people exposed to advertisements, and people not exposed
to advertisements, may be incrementally driven by the advertisements'
campaign.

[0337] As in the survey case, it is expected that within the people in the
exposed condition, those exposed to, for example, particular contents,
times of the day, or from some specific regions, may exhibit an even
higher purchase pattern than others. Attributes such as these, alone or
in combinations, may describe areas of the advertisements inventory where
the system was most effective finding a receptive audience to its
advertisements. These attributes may be in the hundreds, and vary from
type of advertisement to type of advertisement, and may belong to a
number of classes that describe the consumer receiving the advertisement,
such as those of the inventory used to deliver the advertisement, those
relevant to the advert shown (size, concept, color), and the like. The
system may autonomously decide to be more proactive to acquire such areas
of the advertisements inventory, such as through higher bids in a real
time environment, through reporting that can be translated into orders to
buy, in a non-real time environment, and the like. Also, the system might
not look for all of the tens or hundreds of different attributes as
described herein (e.g. particular contents, times of the day, from some
specific regions), it may instead look to optimally allocate budgets,
prices to pay, effective frequency and recency to show ads to consumers,
and the like, within a few well defined segments of the population.

[0338] In embodiments, the system may define a segment as a group of
consumers that share some characteristics. These segments may be
demographic (e.g. women between 25 and 34 year old), have a common
interest (e.g. people who like to collect stamps), be in the market for a
certain product (e.g. people in market to buy a compact car), live in a
certain place (e.g. people living in the vicinity of Atlanta, Ga.), show
an affinity with a brand, and the like. These segments might also be
composed through Boolean expressions of other segments.

[0339] In embodiments, there may be the need to keep a fraction of the
population exposed to advertisements and another group not exposed
advertisements, either by exposing them to public service advertisements,
by not exposing them altogether, by exposing them to ads from a different
brand or product, and the like, where a survey or an off-line metric may
be used, such as purchase behavior used as a signal of goodness.

[0340] By measuring the off-line metric across the group exposed and
unexposed, it may be possible to understand which segment is more
receptive to the message, and what frequency, bid price, and budgets are
most effective. As such, the system may automatically reallocate budgets,
bids, frequencies, and the like, to acquire the advertisements inventory
best suited to drive incremental awareness. Also, the system may include
a mechanism to modify budget allocation to show surveys, as it may have
the capability to detect lower or higher than expected survey response
rates. For example, in the case were the system is expecting to show one
million surveys per week, and receive 1000 answers, if it only receives
500 answers, it may automatically reallocate twice the budget to ensure
1000 answers per week are received. The same mechanism may be applied to
any metric of time to ensure the right spend per unit of time is
allocated, and ensure the right number of survey answers are acquired.
The same mechanism may be applied to any segment or partition of the
population being surveyed, so that, if there are not enough or too many
answers from a certain segment or partition of the population (for
example, not enough survey answers from males, 18-25 year old), the
system will reallocate just enough money to increase the number of
answers, using an automated mechanism, in real time.

[0341] The methods and systems described herein may be deployed in part or
in whole through a machine that executes computer software, program
codes, and/or instructions on a processor. The processor may be part of a
server, client, network infrastructure, mobile computing platform,
stationary computing platform, or other computing platform. A processor
may be any kind of computational or processing device capable of
executing program instructions, codes, binary instructions and the like.
The processor may be or include a signal processor, digital processor,
embedded processor, microprocessor or any variant such as a co-processor
(math co-processor, graphic co-processor, communication co-processor and
the like) and the like that may directly or indirectly facilitate
execution of program code or program instructions stored thereon. In
addition, the processor may enable execution of multiple programs,
threads, and codes. The threads may be executed simultaneously to enhance
the performance of the processor and to facilitate simultaneous
operations of the application. By way of implementation, methods, program
codes, program instructions and the like described herein may be
implemented in one or more thread. The thread may spawn other threads
that may have assigned priorities associated with them; the processor may
execute these threads based on priority or any other order based on
instructions provided in the program code. The processor may include
memory that stores methods, codes, instructions and programs as described
herein and elsewhere. The processor may access a storage medium through
an interface that may store methods, codes, and instructions as described
herein and elsewhere. The storage medium associated with the processor
for storing methods, programs, codes, program instructions or other type
of instructions capable of being executed by the computing or processing
device may include but may not be limited to one or more of a CD-ROM,
DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

[0342] A processor may include one or more cores that may enhance speed
and performance of a multiprocessor. In embodiments, the process may be a
dual core processor, quad core processors, other chip-level
multiprocessor and the like that combine two or more independent cores
(called a die).

[0343] The methods and systems described herein may be deployed in part or
in whole through a machine that executes computer software on a server,
client, firewall, gateway, hub, router, or other such computer and/or
networking hardware. The software program may be associated with a server
that may include a file server, print server, domain server, internet
server, intranet server and other variants such as secondary server, host
server, distributed server and the like. The server may include one or
more of memories, processors, computer readable media, storage media,
ports (physical and virtual), communication devices, and interfaces
capable of accessing other servers, clients, machines, and devices
through a wired or a wireless medium, and the like. The methods, programs
or codes as described herein and elsewhere may be executed by the server.
In addition, other devices required for execution of methods as described
in this application may be considered as a part of the infrastructure
associated with the server.

[0344] The server may provide an interface to other devices including,
without limitation, clients, other servers, printers, database servers,
print servers, file servers, communication servers, distributed servers
and the like. Additionally, this coupling and/or connection may
facilitate remote execution of program across the network. The networking
of some or all of these devices may facilitate parallel processing of a
program or method at one or more location without deviating from the
scope of the invention. In addition, any of the devices attached to the
server through an interface may include at least one storage medium
capable of storing methods, programs, code and/or instructions. A central
repository may provide program instructions to be executed on different
devices. In this implementation, the remote repository may act as a
storage medium for program code, instructions, and programs.

[0345] The software program may be associated with a client that may
include a file client, print client, domain client, internet client,
intranet client and other variants such as secondary client, host client,
distributed client and the like. The client may include one or more of
memories, processors, computer readable media, storage media, ports
(physical and virtual), communication devices, and interfaces capable of
accessing other clients, servers, machines, and devices through a wired
or a wireless medium, and the like. The methods, programs or codes as
described herein and elsewhere may be executed by the client. In
addition, other devices required for execution of methods as described in
this application may be considered as a part of the infrastructure
associated with the client.

[0346] The client may provide an interface to other devices including,
without limitation, servers, other clients, printers, database servers,
print servers, file servers, communication servers, distributed servers
and the like. Additionally, this coupling and/or connection may
facilitate remote execution of program across the network. The networking
of some or all of these devices may facilitate parallel processing of a
program or method at one or more location without deviating from the
scope of the invention. In addition, any of the devices attached to the
client through an interface may include at least one storage medium
capable of storing methods, programs, applications, code and/or
instructions. A central repository may provide program instructions to be
executed on different devices. In this implementation, the remote
repository may act as a storage medium for program code, instructions,
and programs.

[0347] The methods and systems described herein may be deployed in part or
in whole through network infrastructures. The network infrastructure may
include elements such as computing devices, servers, routers, hubs,
firewalls, clients, personal computers, communication devices, routing
devices and other active and passive devices, modules and/or components
as known in the art. The computing and/or non-computing device(s)
associated with the network infrastructure may include, apart from other
components, a storage medium such as flash memory, buffer, stack, RAM,
ROM and the like. The processes, methods, program codes, instructions
described herein and elsewhere may be executed by one or more of the
network infrastructural elements.

[0348] The methods, program codes, and instructions described herein and
elsewhere may be implemented on a cellular network having multiple cells.
The cellular network may either be frequency division multiple access
(FDMA) network or code division multiple access (CDMA) network. The
cellular network may include mobile devices, cell sites, base stations,
repeaters, antennas, towers, and the like. The cell network may be a GSM,
GPRS, 3G, EVDO, mesh, or other networks types.

[0349] The methods, programs codes, and instructions described herein and
elsewhere may be implemented on or through mobile devices. The mobile
devices may include navigation devices, cell phones, mobile phones,
mobile personal digital assistants, laptops, palmtops, netbooks, pagers,
electronic books readers, music players and the like. These devices may
include, apart from other components, a storage medium such as a flash
memory, buffer, RAM, ROM and one or more computing devices. The computing
devices associated with mobile devices may be enabled to execute program
codes, methods, and instructions stored thereon. Alternatively, the
mobile devices may be configured to execute instructions in collaboration
with other devices. The mobile devices may communicate with base stations
interfaced with servers and configured to execute program codes. The
mobile devices may communicate on a peer to peer network, mesh network,
or other communications network. The program code may be stored on the
storage medium associated with the server and executed by a computing
device embedded within the server. The base station may include a
computing device and a storage medium. The storage device may store
program codes and instructions executed by the computing devices
associated with the base station.

[0351] The methods and systems described herein may transform physical
and/or or intangible items from one state to another. The methods and
systems described herein may also transform data representing physical
and/or intangible items from one state to another.

[0352] The elements described and depicted herein, including in flow
charts and block diagrams throughout the figures, imply logical
boundaries between the elements. However, according to software or
hardware engineering practices, the depicted elements and the functions
thereof may be implemented on machines through computer executable media
having a processor capable of executing program instructions stored
thereon as a monolithic software structure, as standalone software
modules, or as modules that employ external routines, code, services, and
so forth, or any combination of these, and all such implementations may
be within the scope of the present disclosure. Examples of such machines
may include, but may not be limited to, personal digital assistants,
laptops, personal computers, mobile phones, other handheld computing
devices, medical equipment, wired or wireless communication devices,
transducers, chips, calculators, satellites, tablet PCs, electronic
books, gadgets, electronic devices, devices having artificial
intelligence, computing devices, networking equipments, servers, routers
and the like. Furthermore, the elements depicted in the flow chart and
block diagrams or any other logical component may be implemented on a
machine capable of executing program instructions. Thus, while the
foregoing drawings and descriptions set forth functional aspects of the
disclosed systems, no particular arrangement of software for implementing
these functional aspects should be inferred from these descriptions
unless explicitly stated or otherwise clear from the context. Similarly,
it will be appreciated that the various steps identified and described
above may be varied, and that the order of steps may be adapted to
particular applications of the techniques disclosed herein. All such
variations and modifications are intended to fall within the scope of
this disclosure. As such, the depiction and/or description of an order
for various steps should not be understood to require a particular order
of execution for those steps, unless required by a particular
application, or explicitly stated or otherwise clear from the context.

[0353] The methods and/or processes described above, and steps thereof,
may be realized in hardware, software or any combination of hardware and
software suitable for a particular application. The hardware may include
a general purpose computer and/or dedicated computing device or specific
computing device or particular aspect or component of a specific
computing device. The processes may be realized in one or more
microprocessors, microcontrollers, embedded microcontrollers,
programmable digital signal processors or other programmable device,
along with internal and/or external memory. The processes may also, or
instead, be embodied in an application specific integrated circuit, a
programmable gate array, programmable array logic, or any other device or
combination of devices that may be configured to process electronic
signals. It will further be appreciated that one or more of the processes
may be realized as a computer executable code capable of being executed
on a machine readable medium.

[0354] The computer executable code may be created using a structured
programming language such as C, an object oriented programming language
such as C++, or any other high-level or low-level programming language
(including assembly languages, hardware description languages, and
database programming languages and technologies) that may be stored,
compiled or interpreted to run on one of the above devices, as well as
heterogeneous combinations of processors, processor architectures, or
combinations of different hardware and software, or any other machine
capable of executing program instructions.

[0355] Thus, in one aspect, each method described above and combinations
thereof may be embodied in computer executable code that, when executing
on one or more computing devices, performs the steps thereof. In another
aspect, the methods may be embodied in systems that perform the steps
thereof, and may be distributed across devices in a number of ways, or
all of the functionality may be integrated into a dedicated, standalone
device or other hardware. In another aspect, the means for performing the
steps associated with the processes described above may include any of
the hardware and/or software described above. All such permutations and
combinations are intended to fall within the scope of the present
disclosure.

[0356] While the invention has been disclosed in connection with the
preferred embodiments shown and described in detail, various
modifications and improvements thereon will become readily apparent to
those skilled in the art. Accordingly, the spirit and scope of the
present invention is not to be limited by the foregoing examples, but is
to be understood in the broadest sense allowable by law.

[0357] All documents referenced herein are hereby incorporated by
reference.